Optimized content generation method and system

ABSTRACT

An optimized fact checking system analyzes and determines the factual accuracy of information and/or characterizes the information by comparing the information with source information. The optimized fact checking system automatically monitors information, processes the information, fact checks the information in an optimized manner and/or provides a status of the information. In some embodiments, the optimized fact checking system generates, aggregates, and/or summarizes content.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation application of co-pending U.S. patentapplication Ser. No. 16/861,457, filed on Apr. 29, 2020, and titled“OPTIMIZED SUMMARIZING AND FACT CHECKING METHOD AND SYSTEM UTILIZINGAUGMENTED REALITY,” which is a continuation application of co-pendingU.S. patent application Ser. No. 15/859,880 (now U.S. Pat. No.10,740,376), filed on Jan. 2, 2018, and titled “OPTIMIZED SUMMARIZINGAND FACT CHECKING METHOD AND SYSTEM UTILIZING AUGMENTED REALITY,” whichis a continuation application of U.S. patent application Ser. No.14/701,757 (now U.S. Pat. No. 9,990,357), filed on May 1, 2015, andtitled “OPTIMIZED SUMMARIZING AND FACT CHECKING METHOD AND SYSTEM,”which is a continuation application of U.S. patent application Ser. No.14/477,009 (now U.S. Pat. No. 9,189,514), filed on Sep. 4, 2014, andtitled “OPTIMIZED FACT CHECKING METHOD AND SYSTEM,” which are all herebyincorporated by reference in their entireties for all purposes.

FIELD OF THE INVENTION

The present invention relates to the field of information analysis. Morespecifically, the present invention relates to the field ofautomatically verifying the factual accuracy of information.

BACKGROUND OF THE INVENTION

Information is easily dispersed through the Internet, television, socialmedia and many other outlets. The accuracy of the information is oftenquestionable or even incorrect. Although there are many fact checkers,they typically suffer from issues.

SUMMARY OF THE INVENTION

An optimized fact checking system analyzes and determines the factualaccuracy of information and/or characterizes the information bycomparing the information with source information. The optimized factchecking system automatically monitors information, processes theinformation, fact checks the information in an optimized manner and/orprovides a status of the information.

The optimized fact checking system provides users with factuallyaccurate information, limits the spread of misleading or incorrectinformation, provides additional revenue streams, and supports manyother advantages.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a flowchart of a method of implementing optimizedfact checking according to some embodiments.

FIG. 2 illustrates a block diagram of an exemplary computing deviceconfigured to implement the optimized fact checking method according tosome embodiments.

FIG. 3 illustrates a network of devices configured to implement factchecking according to some embodiments.

FIG. 4 illustrates a flowchart of a method of summarizing and factchecking according to some embodiments.

FIG. 5 illustrates a flowchart of a method of fact checking utilizedwith news generation according to some embodiments.

FIG. 6 illustrates a flowchart of a method of news aggregation,summarization and fact checking according to some embodiments.

FIG. 7 illustrates a flowchart of a method of fact checking utilizedwith news aggregation and news generation according to some embodiments.

FIG. 8 illustrates a flowchart of a method of utilizing multiple itemsof content to generate a summary or new content according to someembodiments.

FIG. 9 illustrates a flowchart of a method of utilizing fact checking togenerate content according to some embodiments.

FIG. 10 illustrates a flowchart of a method of utilizing fact checkingfor summarization according to some embodiments.

FIG. 11 illustrates a flowchart of a method of utilizing languagedifferences in generating, aggregating, summarizing and/or fact checkingaccording to some embodiments.

FIG. 12 illustrates a flowchart of a method of generating and factchecking content according to some embodiments.

FIG. 13 illustrates a flowchart of a method of aggregating and factchecking content according to some embodiments.

FIG. 14 illustrates a flowchart of a method of summarizing and factchecking content according to some embodiments.

FIG. 15 illustrates a flowchart of a method of fact checking content andgenerating content according to some embodiments.

FIG. 16 illustrates a flowchart of a method of fact checking content andaggregating content according to some embodiments.

FIG. 17 illustrates a flowchart of a method of fact checking content andsummarizing content according to some embodiments.

FIG. 18 illustrates a flowchart of a method of generating, aggregating,summarizing and/or fact checking content according to some embodiments.

FIG. 19 illustrates a flowchart of a method of generating, aggregating,summarizing and/or fact checking content according to some embodiments.

FIG. 20 illustrates a flowchart of a method of generating, aggregating,summarizing and/or fact checking content according to some embodiments.

FIG. 21 illustrates a diagram of exemplary implementations usable withor without optimized fact checking according to some embodiments.

FIG. 22 illustrates a diagram of an augmented reality device accordingto some embodiments.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

An optimized fact checking system determines the factual accuracy ofinformation by comparing the information with source information.Additional analysis is able to be implemented as well such assummarizing the information, aggregating information and generatinginformation.

FIG. 1 illustrates a flowchart of a method of implementing optimizedfact checking according to some embodiments.

In the step 100, information is monitored. In some embodiments, allinformation or only some information (e.g., a subset less than all ofthe information) is monitored. In some embodiments, only explicitlyselected information is monitored. In some embodiments, although allinformation is monitored, only some information (e.g., informationdeemed to be fact-based) is fact checked. In some embodiments, the stepof monitoring is skipped.

The information includes, but is not limited to, broadcast information(e.g., television broadcast information, radio broadcast information),email, documents, articles, news reports, database information, socialnetworking/media content (tweets/Twitter®, Facebook® postings),webpages, message boards, web logs, any computing device communication,telephone calls/communications, audio, text, live speeches/audio, radio,television video/text/audio, VoIP calls, video chatting, videoconferencing, images, videos, and/or any other information. Theinformation is able to be in the form of phrases, segments, sentences,numbers, words, comments, values, graphics, and/or any other form.

In some embodiments, monitoring includes recording, scanning, capturing,transmitting, tracking, collecting, surveying, and/or any other type ofmonitoring. In some embodiments, monitoring includes determining if aportion of the information is able to be fact checked. For example, ifinformation has a specified structure, then it is able to be factchecked.

In some embodiments, the optimized fact checking system is implementedwithout monitoring information. This is able to be implemented in anymanner. For example, while information is transmitted from a source, theinformation is also processed and fact checked so that the fact checkresult is able to be presented. In some embodiments, the fact checkresult is embedded in the same stream as the information. In someembodiments, the fact check result is in the header of a packet.

In the step 102, the information is processed. Processing is able toinclude many aspects including, but not limited to, converting (e.g.,audio into text), formatting, parsing, determining context,transmitting, converting an image into text, analyzing andreconfiguring, and/or any other aspect that enables the information tobe fact checked in an optimized manner. Parsing, for example, includesseparating a long speech into separate phrases that are each separatelyfact checked. For example, a speech may include 100 different facts thatshould be separately fact checked. In some embodiments, the step 102 isable to be skipped if processing is not necessary (e.g., text may notneed to be processed). In some embodiments, processing includesconverting the information into a searchable format. In someembodiments, processing occurs concurrently with monitoring. In someembodiments, processing includes capturing/receiving and/or transmittingthe information (e.g., to/from the cloud).

In a specific example of processing, information is converted intosearchable information (e.g., audio is converted into searchable text),and then the searchable information is parsed into fact checkableportions (e.g., segments of the searchable text; several word phrases).

Parsing is able to be implemented in any manner including, but notlimited to, based on sentence structure (e.g., subject/verbdetermination), based on punctuation including, but not limited to, endpunctuation of each sentence (e.g., period, question mark, exclamationpoint), intermediate punctuation such as commas and semi-colons, basedon other grammatical features such as conjunctions, based on capitalletters, based on a duration of a pause between words (e.g., 2 seconds),based on duration of a pause between words by comparison (e.g., typicalpauses between words for user are 0.25 seconds and pauses betweenthoughts are 1 second)—the user's speech is able to be analyzed todetermine speech patterns such as length of pauses between words lastinga fourth of the length for pauses between thoughts or sentences, basedon a change of a speaker (e.g., speaker A is talking, then speaker Bstarts talking), based on a word count (e.g., 10 word segments), basedon speech analysis, based on a slowed down version (recording thecontent, slowing down the recorded content to determine timing breaks),based on keywords/key phrases, based on search results, and/or any othermanner. In some embodiments, processing includes, but is not limited to,calculating, computing, storing, recognition, speaker recognition,language (word, phrase, sentence, other) recognition, labeling, and/orcharacterizing.

In the step 104, the information is fact checked in an optimized manner.In some embodiments, instead of or in addition to fact checking, methodssuch as generating, aggregating, and/or summarizing are implemented.Fact checking includes comparing the information to source informationto determine the factual validity, accuracy, quality, character and/ortype of the information. In some embodiments, the source informationincludes web pages on the Internet, one or more databases, dictionaries,encyclopedias, social network information, video, audio, any othercommunication, any other data, one or more data stores and/or any othersource.

In some embodiments, the comparison is a text comparison such as astraight word for word text comparison. In some embodiments, thecomparison is a context/contextual comparison. In some embodiments, anatural language comparison is used. In some embodiments, patternmatching is utilized. In some embodiments, an intelligent comparison isimplemented to perform the fact check. In some embodiments, exact match,pattern matching, natural language, intelligence, context, and/or anycombination thereof is used for the comparison. Any method of analyzingthe source information and/or comparing the information to the sourceinformation to analyze and/or characterize the information is able to beimplemented. An exemplary implementation of fact checking includessearching (e.g., a search engine's search), parsing the results orsearching through the results of the search, comparing the results withthe information to be checked using one or more of the comparisons(e.g., straight text, context or intelligent) and retrieving resultsbased on the comparison (e.g., if a match is found return “True”). Thecomparison is able to involve comparing a text string found in thetarget information with source information to determine if a match isfound, or comparing a pattern of content with patterns in sourcecontent, or any other implementation. The results are able to be anytype including, but not limited to, binary, Boolean (True/False), text,numerical, and/or any other format. In some embodiments, determiningcontext and/or other aspects of converting could be implemented in thestep 104. In some embodiments, the comparison for fact checking includesdetermining if a source agrees with (or confirms) or disagrees with (ordisproves) the target information, and then the number of agreeingsources is compared with the number of disagree sources. For example, if10 sources agree and 5 disagree with the target information, then theresult of the fact check is true. In some embodiments, a source isconsidered to agree if the searched for content is found in the source(e.g., a match is found), and a source is considered to disagree if thesearched for content is not found in the source or if an oppositecontent is found (e.g., same search string but with a “not” in front ofit). In some embodiments, fact checking utilizes previously fact checkedsources (fact checked manually and/or automatically), and information(e.g., segments, phrases, sentences) from the sources is classified asaccurate or factually inaccurate, then information being fact checked iscompared with the classified source information, and if a match is foundin a classification, then the value of that classification is returned,and if a match is not found, then a result such as “unknown” isreturned. For example, “X was the president during the war” is factchecked, and the phrase is found in the factually inaccurateclassification, so a result of false or factually inaccurate isreturned. In another example of fact checking, a set of data iscollected/established. In some embodiments, the collected data islimited to fact checked data or data where the inaccurate content hasbeen removed or classified separately. Then, any one of the searches(e.g., exact, text, pattern, intelligent) is used to find a match in thecollected source data, and if the number of matches is above athreshold, a result of true is returned, otherwise, false is returned. Aconfidence score is able to be generated in any manner; for example, aconfidence score is based on the number of sources that agree ordisagree with the information—10 sources agree/match with theinformation and 0 disagree=100% confidence score or 9 sources agree withthe information and 1 disagrees=90% confidence score. In someembodiments, the sources are rated and/or weighted. For example, sourcesare able to be given more weight based on accuracy of the source, typeof the source, user preference, user selections, classification of thesource, and/or any other weighting factor. The weighting is then able tobe used in determining the fact check result. For example, if a highlyweighted or rated source agrees with a comment, and a low weightedsource disagrees with the comment, the higher weighted source is used,and “valid” or a similar result is returned.

For example, if information is being fact checked using comparison, thecomparisons may result in sets of results (e.g., two different sets ofresults are found—a first set determines the information is true, andthe second set determines the information is false), the set with thehigher rated sources or source score is selected. A sample equation is:

Comparison Score=Number of Sources*Average Rating of Sources, where thecomparison with highest Comparison Score determines the verificationresult. For example, “Corporation Z is a polluter,” results in 100sources with an average rating of 8 (where 1 is untrustworthy and 10 isvery trustworthy) saying “True,” and 5 sources with an average rating of3 saying “False,” the result would be “True” since (100*8)=800 isgreater than (5*3)=15.

In the step 106, a status of the information isprovided/presented/displayed based on the fact check result. The statusis provided in any manner including, but not limited to, transmittingand/or displaying text, highlighting, underlining, color effects, avisual or audible alert or alarm, a graphical representation, and/or anyother indication. The meaning of the status is able to be any meaningincluding, but not limited to, correct, incorrect, valid, true, false,invalid, opinion, hyperbole, sarcasm, hypocritical, comedy, unknown,questionable, suspicious, need more information, depends, misleading,deceptive, possibly, close to the truth, and/or any other status.

The status is able to be presented in any manner, including, but notlimited to, lights, audio/sounds, highlighting, text, a text bubble, ascrolling text, color gradient, headnotes/footnotes, an iconic orgraphical representation, a video or video clip, music, other visual oraudio indicators, a projection, a hologram, a tactile indicatorincluding, but not limited to, vibrations, an olfactory indicator, aTweet, a text message (SMS, MMS), an email, a page, a phone call, asocial networking page/transmission/post/content, or any combinationthereof. For example, text is able to be highlighted or the text coloris able to change based on the validity of the text. For example, as auser types a social network message, the true statements are displayedin green, the questionable statements are displayed in yellow, and thefalse statements are displayed in red. In some embodiments, providingthe status includes transmitting and/or broadcasting the status to oneor more devices (e.g., televisions). In another example, a web pageand/or social networking content includes markings such as strikethroughor highlighting and text embedded/overlaid, so that the user is able toread a webpage with the fact check results displayed.

The status is also able to include other information including, but notlimited to, statistics, citations and/or quotes. Providing the status ofthe information is also able to include providing additional informationrelated to the fact checked information, such as an advertisement. Insome embodiments, providing includes pointing out, showing, displaying,recommending, playing, presenting, announcing, arguing, convincing,signaling, asserting, persuading, demonstrating, denoting, expressing,hinting, illustrating, implying, tagging, labeling, characterizing,and/or revealing.

In some embodiments, the optimized fact checking system is implementedsuch that responses, validity determinations and/or status presentationsare available in real-time or near real-time. By real-time, it is meantinstantaneously (e.g., within 1 second); whereas near real-time iswithin a few seconds (e.g., within 5 seconds). Furthermore, since themonitoring, processing, fact checking and providing status are all ableto be performed automatically without user intervention, real-time alsomeans faster than having a human perform the search and presentingresults. Depending on the implementation, in some embodiments, the factcheck result, summary, generated story, aggregation, and/or otherinformation is presented in at most 1 second, at most several seconds(e.g., at most 5 seconds), at most a minute (not real-time), at mostseveral minutes or by the end of a show/content. In some embodiments,the time amount (e.g., at most 1 second) begins once a user pauses intyping, once a phrase has been communicated, once a phrase has beendetermined, at the end of a sentence, once an item is flagged, oranother point in a sequence. For example, as soon as a phrase isdetected, the optimized fact checking system checks the fact, returns aresult and displays an indication based on the result in less than 1second—clearly much faster than a human performing a search, analyzingthe search results and then typing a result to be displayed on a screen.

In some embodiments, fewer or more steps are implemented. Furthermore,in some embodiments, the order of the steps is modified. In someembodiments, the steps are performed on the same device, and in someembodiments, one or more of the steps, or parts of the steps, areseparately performed and/or performed on separate devices. In someembodiments, each of the steps 100, 102, 104 and 106 occur or are ableto occur in real-time or non-real-time. Any combination of real-time andnon-real-time steps is possible such as all real-time, none real-timeand everything in between.

FIG. 2 illustrates a block diagram of an exemplary computing device 200configured to implement the optimized fact checking method according tosome embodiments. The computing device 200 is able to be used toacquire, store, compute, process, communicate and/or display informationincluding, but not limited to, text, images, videos and audio. In someexamples, the computing device 200 is able to be used to monitorinformation, process the information, fact check the information,generate, aggregate, summarize, and/or provide a status of theinformation. In general, a hardware structure suitable for implementingthe computing device 200 includes a network interface 202, a memory 204,a processor 206, I/O device(s) 208, a bus 210 and a storage device 212.The choice of processor is not critical as long as a suitable processorwith sufficient speed is chosen. The memory 204 is able to be anyconventional computer memory known in the art. The storage device 212 isable to include a hard drive, CDROM, CDRW, DVD, DVDRW, flash memorycard, solid state drive or any other storage device. The computingdevice 200 is able to include one or more network interfaces 202. Anexample of a network interface includes a network card connected to anEthernet or other type of LAN. The I/O device(s) 208 are able to includeone or more of the following: keyboard, mouse, monitor, display,printer, modem, touchscreen, touchpad, speaker/microphone, voice inputdevice, button interface, hand-waving, body-motion capture, touchless 3Dinput, joystick, remote control, brain-computer interface/direct neuralinterface/brain-machine interface, camera, and other devices. In someembodiments, the hardware structure includes multiple processors andother hardware to perform parallel processing. Fact checkingapplication(s) 230 used to perform the monitoring, processing, factchecking and providing are likely to be stored in the storage device 212and memory 204 and processed as applications are typically processed.More or fewer components shown in FIG. 2 are able to be included in thecomputing device 200. In some embodiments, fact checking hardware 220 isincluded. Although the computing device 200 in FIG. 2 includesapplications 230 and hardware 220 for implementing the fact checking,the fact checking method is able to be implemented on a computing devicein hardware, firmware, software or any combination thereof. For example,in some embodiments, the fact checking applications 230 are programmedin a memory and executed using a processor. In another example, in someembodiments, the fact checking hardware 220 is programmed hardware logicincluding gates specifically designed to implement the method.

In some embodiments, the fact checking application(s) 230 includeseveral applications and/or modules. Modules include a monitoring modulefor monitoring information, a processing module for processing (e.g.,converting) information, a generating module for generating a story orother information, an aggregating module for aggregating information, asummarizing module for summarizing information, a fact checking modulefor fact checking information and a providing module for providing astatus of the information. In some embodiments, modules include one ormore sub-modules as well. In some embodiments, fewer or additionalmodules are able to be included. In some embodiments, the applicationsand/or the modules are located on different devices. For example, adevice performs monitoring, processing, and fact checking, but theproviding is performed on a different device, or in another example, themonitoring and processing occurs on a first device, the generating,aggregating, summarizing and/or fact checking occurs on a second deviceand the providing occurs on a third device. Any configuration of wherethe applications/modules are located is able to be implemented such thatthe fact checking system is executed.

Examples of suitable computing devices include, but are not limited to apersonal computer, a laptop computer, a computer workstation, a server,a mainframe computer, a handheld computer, a personal digital assistant,a pager, a telephone, a fax machine, a cellular/mobile telephone, asmart appliance, a gaming console, a digital camera, a digitalcamcorder, a camera phone, a smart phone/device (e.g., a Droid® or aniPhone®), a portable music player (e.g., an iPod®), a tablet (e.g., aniPad®), a video player, an e-reader (e.g., Kindle™), a DVDwriter/player, an HD (e.g., Blu-ray®) or ultra high densitywriter/player, a television, a copy machine, a scanner, a car stereo, astereo, a satellite, a DVR (e.g., TiVo®), a smart watch/jewelry, smartdevices, a home entertainment system or any other suitable computingdevice.

In some embodiments, the systems described herein are implemented usinga device with specialized components such as a specialized fact checkingmicroprocessor configured to fact check content, a specialized newsgeneration microprocessor configured to generate stories, and/or aspecialized summarization microprocessor with self-contained lexicalchaining resources such as a synonym database. In some embodiments, thesystems described herein are implemented using a device with specializedcomponents such as a specialized fact checking memory configured tostore fact check sources and/or results, a specialized news generationmemory configured to store content for generating stories, and/or aspecialized summarization memory configured to store self-containedlexical chaining resources such as a synonym database.

In another example, a camera phone device is configured for implementingthe methods described herein. The camera phone device includes a sensorfor acquiring content (e.g., photos/videos), an image processorconfigured for processing the image content, and a memory for storingthe image content. Additionally, the camera phone device includes aprocessor and additional memory for processing and storing contentincluding fact checking, summarizing, aggregating, and/or generatingcontent. Although the device described is a camera phone, any devicewith a camera (e.g., a tablet, smart watch) is able to be utilized. Inan exemplary implementation, the camera device captures an image,processes, displays the image is on a display, additionally, generating,aggregating, summarizing and/or fact checking are implemented on thecamera device using either the captured image or other content.

In another example, a device is configured to couple with a cameradevice. For example, the device is a computing device with a USB portwhich is able to receive a cable which couples to a video/camera/webcamdevice. In some embodiments, a device includes a camera device or isconfigured to couple with a camera device.

FIG. 3 illustrates a network of devices configured to implement factchecking according to some embodiments. The network of devices 300 isable to include any number of devices and any various devices including,but not limited to, a computing device (e.g., a tablet) 302, atelevision 304, a smart device 306 (e.g., a smart phone) and a source308 (e.g., a database) coupled through a network 310 (e.g., theInternet). The source device 308 is able to be any device containingsource information including, but not limited to, a searchable database,web pages, transcripts, statistics, historical information, or any otherinformation or device that provides information. The network 310 is ableto any network or networks including, but not limited to, the Internet,an intranet, a LAN/WAN/MAN, wireless, wired, Ethernet, satellite, acombination of networks, or any other implementation of communicating.The devices are able to communicate with each other through the network310 or directly to each other. One or more of the devices is able to bean end user device, a media organization, a company and/or anotherentity. In some embodiments, peer-to-peer sourcing is implemented. Forexample, the source of the data to be compared with is not on acentralized source but is found on peer sources.

FIG. 4 illustrates a flowchart of a method of summarizing and factchecking according to some embodiments. In the step 400, information(e.g., target information) is monitored. In the step 402, theinformation is processed/analyzed. In the step 404, the information issummarized. In some embodiments, the information is summarized usinglexical chaining or another summarization implementation. In someembodiments, the summary includes selected sentences based on thelexical chaining. For example, 5 sentences are selected using lexicalchaining. In some embodiments, the sentences are unedited, and in someembodiments, the sentences are edited (e.g., only segments of sentencesare selected). In the step 406, the summary generated is fact checked bycomparing the summary with source information. For example, the summaryis separated into segments which are fact checked by comparing thesegments with source information. In the step 408, the summary and/orfact check results are displayed. In some embodiments, fewer oradditional steps are implemented. For example, in addition to factchecking the summarized information, the entire target information (or alarger portion than the summary) is fact checked as well. In someembodiments, the order of the steps is modified. For example, in someembodiments, the information is fact checked, and then the fact checkedinformation is summarized. For example, only content determined to befactually accurate is summarized or only content determined to befactually inaccurate is summarized, or another characterization issummarized.

In some embodiments, lexical chaining is utilized to summarize targetinformation. For example, a video is monitored or a web page isanalyzed, and instead of or in addition to fact checking themonitored/analyzed information, a summary is generated using lexicalchaining. The generated summary is fact checked by comparing the summaryor portions thereof with source information.

In some embodiments, lexical chaining is utilized to summarize sourceinformation. For example, instead of or in addition to storing entireweb pages, articles, transcripts, and/or other source information to beused to compare with target information, summaries or lexical chains ofthe source information are generated and stored. In some embodiments,only summaries are stored, and only the summaries are used for comparingwith target information. In some embodiments, the summaries aregenerated and used but not stored. In some embodiments, summaries arestored with the full source information, and the summaries are used in apreliminary comparison to determine if a source is useful, and then thefull text is used for further comparison to determine a fact checkresult.

A lexical chain is a sequence or collection of related words ormultiword phrases. The related words are able to be close in proximity(e.g., adjacent words or words in the same sentence) or not close (e.g.somewhere in the same document). Words are able to be considered relatedin any manner such as based on a thesaurus. A lexical chain is able tobe dependent on or independent of grammatical structure. A lexical chainreceives a score based on the length of the chain and/or therelationships between the chain items (e.g., same word, antonym,synonym, meronym, hypernym, holonym). The lexical chain is able to beused to determine topical elements in a document which are then able tobe used to summarize the document. A lexical chain with a higher scorerepresents a key concept whereas a lower score is a minor point.

Lexical chaining is implemented in any manner. In some embodiments,lexical chaining utilizes one or more sources such as: the WordNetthesaurus, a segmentation algorithm, a part of speech tagging system,and a parser. In some embodiments, relationships between words areidentified for lexical chaining. An example of generating lexical chainsincludes: selecting a set of candidate words/phrases, finding anappropriate chain using relatedness criteria among members of the chainsfor each candidate word, and inserting a word in the chain if a word isfound. In some embodiments, the set of selected candidate words is basedon social networking information or other user information. For example,a user's social networking page is analyzed and keywords are determined(e.g., based on use count or lexical chaining of the social networkinginformation), and the determined keywords are used as the set ofselected keywords. Another example of generating lexical chains isimplemented without selecting candidate words/phrases. Factors areanalyzed when determining the strength of the chain such as repetition,density, relationship between words, position within text, and length.Chains are able to be scored in any manner. In some embodiments, chainscores are determined by testing/training and providing a manual (human)score to each chain. In some embodiments, several lexical chains aregenerated, and each are used to generate a summary, which are thenreviewed and rated manually (e.g., by a human), and the lexical chainthat generated the best summary is selected for further use. In someembodiments, the chain score/strength is based on the length of thechain which is based on the number of occurrences of members of thechain. In some embodiments, a homogeneity index is analyzed to determinethe strength of the chain such that the index is based on the number ofdistinct occurrences divided by the length. After chains are scored,they are able to be compared/ranked (e.g., top ranked chain has thehighest score). The highest score chains are considered to be thestrongest chains; for example, all chains with a score above a thresholdare considered strong chains. Once strong chains have been determined,full sentences or phrases are extracted based on the chains. In oneexample, a sentence with the first appearance of a member in a strongchain (or the strongest chain) is extracted. In another example, asentence with the most members of a strong chain (or the strongestchain) is extracted. In some embodiments, a single sentence is extractedfor each strong chain, and in some embodiments, multiple sentences areable to be extracted for each strong chain if the number of members ineach sentence is above a threshold. In some embodiments, a list oflexical chains is generated, and for each lexical chain, a list oflocations in the document referencing the lexical chain, page/linenumbers or other location data, and/or a number/score denoting thecontribution of the chain to the representation of the document aregenerated.

In some embodiments, lexical chaining utilizes/incorporates fact checkresults/information. For example, information is fact checked, and onlyfactually accurate sentences are analyzed for lexical chaining. Inanother example, only factually inaccurate sentences are analyzed forlexical chaining. In some embodiments, all sentences are analyzed forlexical chaining, but then only factually accurate sentences or onlyfactually inaccurate sentences of the sentences with the strongestchains are extracted. In some embodiments, the factual accuracy of asentence is combined with the lexical chain strength to generate a totalsentence strength. For example, if a sentence is identified by a stronglexical chain (+1), but the sentence is factually inaccurate (−1), thenthe total sentence strength is 0. In another example, if a sentence isidentified by a strong lexical chain (+1), and the sentence is factuallyaccurate (+1), then the total sentence strength is +2. In anotherexample, if a sentence is identified by a strong lexical chain (+1), andthe sentence is questionable (0), then the total sentence strength is+1. Fact checking results/analysis are able to be used with lexicalchaining in any manner.

In some embodiments, different summarizing implementations are utilized,the results are compared, and based on the comparison, a summarizingimplementation is selected for future use. For example, summarizingimplementation 1 and summarizing implementation 2 both summarize severalarticles, and summarizing implementation 1 is selected based on thecomparison.

In some embodiments, summarization is implemented utilizing lexicalchaining: the content is parsed/segmented as described herein, lexicalchains are generated, strong chains are identified, andsignificant/strong sentences are extracted. In addition to or as part oflexical chaining, word distribution/word counts, cue phrase methodswhich use meta-linguistic markers, location methods, and/or formattingmethods are utilized. For example, the number of times each word is usedis counted, if the words are used under a heading is determined, wherethe words are located (e.g., beginning, middle, end of a document) isanalyzed, and/or if the words are bolded, underlined, italicized orgiven any other special format or marker is utilized. Word frequency isable to be a helpful indication of important concepts. Taking intoaccount word relations is able to increase the quality of the analysis.

In simplified embodiments of summarization and fact checking, the numberof instances of each word is counted in the content (e.g., the word“taxes” is counted 15 times, “raising” is counted 10 times, and“employment” is counted 8 times). Then, sentences and/or phrases withthe top (e.g., top 3—taxes, raising, employment) words counted areextracted. In some embodiments, only sentences with two or more of thetop words are extracted, or only sentences with all of the top words areextracted. The extracted sentences are then fact checked by comparingthem with source information, and a result of the fact check isprovided. In some embodiments, all sentences with any of the top wordsare fact checked, and if the fact check result is factually accurate,then the sentence is extracted, but if the sentence is factuallyinaccurate, then the sentence is not extracted.

In some embodiments, summarizing utilizes social networking informationor other information of a user to determine a focus of the informationbeing summarized. For example, a user's social networking content (e.g.,Facebook® pages, tweets) is analyzed, interests are determined using anyinterest determination analysis (e.g., locating frequently used words,based on “likes,” based on sites visited), the interests are stored in adata structure, and the interests are utilized when performing thesummarization analysis. For example, when performing lexical chaining,the chains are given strength values based on standard analysis, butthen the chains are given additional strength based on the userinformation. Furthering the example, if lexical chaining determines“baseball” is in the strongest chain of an article, and “49ers” in asecond strongest chain, and “49ers” is one of the user's interests, thenthe “49ers” chain strength is modified to be the strongest chain. Themodification of chain strength is able to be implemented in any mannerusing any calculations. For example, if a word or phrase in a lexicalchain is also in the user interest data structure, that lexical chain isput at the top of the strength list, and if multiple chains match itemsin the user interest data structure, then they are put at the top of thestrength list but their chaining order is maintained among them. Inanother example, the user interest data structure includesvalues/weights, such that the user interest weight is added to the chainstrength to compute a final strength. For example, some user interestsare stronger user interests such as something the user loves versussomething the user is mildly interested in, so the stronger userinterests are given more weight than mild interests. The modified orfinal strengths are then able to be used in generating the summary. Forexample, the sentences with the highest final strengths are retained asthe summary.

In focused embodiments of fact checking, headings are detected within adocument (e.g., in a resume, the heading “work history”), and onlycontent within that heading area is fact checked (e.g., the contentbetween “work history” and “education”). For example, content isanalyzed, headings are detected (e.g., based on keywords such as“employment” or “work history,” based on specialized formatting such aslarger font, bold, underline, and/or based on metadata), content betweenspecific headings is fact checked, and results are provided. Furtheringthe example, a data structure stores typical resume headings such as“education,” “work history,” “certifications,” and/or any otherheadings. If a specific section is intended to be fact checked such asthe “work history” section, then the content is analyzed by searchingfor “work history.” After the heading “work history” is located, thatlocation is stored as the beginning of the content to be fact checked.Then, another heading is searched for. Once any additional heading islocated, that location immediately preceding the heading is stored asthe end of the content to be fact checked. The content between theheadings is able to be extracted and/or fact checked.

FIG. 5 illustrates a flowchart of a method of fact checking utilizedwith news (or any kind of story/content) generation according to someembodiments. In the step 500, information is extracted from one or moredata sources. For example, information from a user's resume isextracted. The information from the resume is able to be the user'sname, most recent occupation, most recent employer, schooling, and/orany other information. In some embodiments, the extracted information isstored in a data structure such as a database. The system is able toautomatically place the extracted information into appropriate cells inthe data structure. For example, the user's name is placed in a “name”cell, and the user's occupation is stored in an “occupation” cell. Thesystem is able to determine which information is stored where in anymanner, such as based on keyword analysis, location of the informationin a document analysis, and/or any other manner. In some embodiments,the information is only factually accurate information as determined byfact checking, only factual information, such as numbers/statistics,and/or other factually verifiable information. In some embodiments, theinformation extracted is fact checked. In some embodiments, theinformation is fact checked before or after it is extracted. In someembodiments, only factually accurate information is extracted/retained.In some embodiments, a fact check status of the extracted information isstored and indicated. In some embodiments, the information extracted isanalyzed to determine if it is factual information versus another typeof information such as opinion by pattern analysis, language analysisand/or any other analysis. In the step 502, the extracted information isorganized into a narrative. Organizing includes generating content andincluding the extracted information in appropriate locations within thecontent. For example, templates of text are utilized, and locations(e.g., placeholders) are reserved which are filled in with extractedinformation. Continuing with the resume example, a template includes:“*name* is a *occupation* who is working at *employer*. *name* graduatedfrom *college*.” The items such as *name*, *occupation*, *employer* and*college* are input using the extracted information. Other text such as“who is working at” is able to be retrieved from the template or a datastructure which stores the information, so that it is able to beretrieved and incorporated into a story that is readable by a human. Inanother example of a template, a template includes pre-drafted text withplace holders which are automatically replaced by retrieved/extractedcontent. In another example of a template, a data structure storespre-drafted text and placeholders, and a method is designed to utilizethe pre-drafted text at specified times/places and fill in and utilizethe placeholders with extracted content. Templates are also able tostore other content such as images, videos, sounds, advertisementsand/or any other content. Any template, method and/or implementation isable to be utilized to generate content. In some embodiments, fewer oradditional steps are implemented. For example, in some embodiments,artificial intelligence is utilized to incorporate emotions and/or apersonality into the narrative. For example, instead of placing datainto place-holders embedded in text, artificial intelligence is utilizedto make the article even more human-friendly such as by providinganecdotes, commentary, and/or historical references. The artificialintelligence is able to be implemented in any manner such as byinputting additional information into a data structure which the systemretrieves at random or determined times, and/or based on acquiringadditional information on the Internet. For example, the system analyzesa user's social networking page and determines that the user is a 49ersfan (e.g., by searching for and locating specific words/phrases, bydetermining words/phrases most commonly used by the user such as basedon word use frequency, or the user information is manually input), sothe system utilizes a database containing references to the 49ers, andat random or specified points in an article interjects a comparison orreference to the 49ers. Furthering the example, if the user is readingan automatically computer-generated article about football, a sentenceis added specifically regarding the 49ers. In some embodiments, thesocial networking information and/or other acquired information aboutthe user (e.g., preferences, history, occupation, finances, purchases)is utilized to determine the focus (e.g., based on a keyword used mostoften or another classification analysis) of the article beinggenerated. For example, if the article being generated utilizes datasources with information from all 32 football teams, the article is ableto focus on information relevant to the 49ers. For example, a databasestores one or more keywords or user focuses in different classifications(e.g., football->49ers, economics->taxes, environment->global warming,pollution), and when generating content, the original content isclassified or was previously classified, and the original contentclassification is matched with the user classifications, and keywords inthe matching classification are utilized when generating content, e.g.,lexical chaining gives sentences with the keyword extra weight or onlysentences containing the keyword or related to the keyword such asspatially or topically are utilized for analysis such as lexicalchaining. For example, an original content article was classified as“football,” so the “football” user classification is found, whichcontains the “49ers,” and when the original content article issummarized, the sentences with the word “49ers” are used. In someembodiments, the order of the steps is modified.

In some embodiments, templates for use with automatic content generationare automatically generated. For example, an author/speaker/other entityis determined by name recognition, facial recognition, locationrecognition and/or any other manner, then text from a data structure isretrieved based on the detected entity (e.g., detect a politician'sname, and take text from a “politician” template which has specific textand placeholders), then the placeholders are filled in with acquiredinformation (e.g., from a database, a speech or an article). In anotherexample of automatically generating a template, a template is generatedbased on the focus/topic of content (e.g., determine topic based onkeyword analysis such as the most frequently used keyword). In someembodiments, the template to utilize is user-specific. For example, inaddition to or instead of a template for politicians, there is atemplate for users who are Conservative, and there is a second templatefor users who are Liberal. Furthering the example, when a Conservativeis speaking, a Liberal person receives an article or summary of thefactually inaccurate content from the Conservative, since the Liberalperson may be more focused on finding the misinformation provided by theperson with an opposing perspective. To utilize the user-specifictemplate, users are classified. Generated content is also classified.Content (e.g., an article) is summarized multiple different ways (e.g.,only factually accurate content, only factually inaccurate content, orcontent containing specified keywords is summarized), and the differentsummaries/generated content are stored in specific classifications.Then, the generated content and/or summaries are displayed/distributedbased on the user classification and content classification (e.g., usingmatching classifications).

In some embodiments, generating, aggregating, summarizing and/or factchecking are based on details of the user (e.g., age, sex, preferences,hobbies, education, financial status, occupation, political affiliation,and/or any other details about the user). For example, specifictemplates are generated/used for each occupation, such that the specifictemplate is utilized when content is generated. Furthering the example,the user is a doctor, and an article about taxes is being automaticallygenerated using a recently published report. Several different templates(e.g., doctor, lawyer, unemployed) are/were generated for the article orfor articles in general. The “doctor” template is used to tailor thearticle for the user who is a doctor. For example, if there is aspecific tax on medical items, if medicare taxes are affected, or a taxbracket for wealthy people is increased, these items are included in thetemplate and the generated article, whereas an “unemployed” templatewould include or focus on different aspects from the report. Thespecific text stored in the template and/or the placeholders are able tobe template dependent. In some embodiments, content generated is genericcontent and user-specific content. For example, a generic article isgenerated which is applicable to all users, and user-specific contentwhich is specific to the user or the type of user is generated. In someembodiments, the user information is utilized to determine what sourceinformation to use to generate the content. In some embodiments, theuser information is utilized to determine what content to aggregate. Forexample, a keyword for a user (e.g., doctor) is utilized to matcharticles with the matching keyword. The user's information is able to beacquired, known, and/or used in any manner such as based on socialnetwork information, provided information, stored information, recentpurchases, visited web sites, and/or channels watched.

In some embodiments, content (news) generation highlights key aspects ofa topic. For example, items that are determined to be focal points orimportant points are included in the generated content. The key aspectsare determined in any manner such as based on fact checking, based onheadings, fonts, location of content, and/or any other stylisticinformation, based on social networking information (e.g., number oftimes sentence is re-tweeted), and/or any other manner. For example,based on social networking information (e.g., by crawling a user'sFacebook® page), it is determined a user is a 49ers fan, so when a storyis generated, the content retrieved is focused on the 49ers. A headlineand/or photo is able to be generated and/or provided with the generatedcontent. In some embodiments, the generated content is based oninformation provided by a person or people at the top of theirprofession such as CEOs. For example, a Twitter account of CEO ismonitored, analyzed and content is generated using the tweets of theCEO. Furthering the example, the account information is fact checked,and the factually accurate information is used to generate new content,and/or the fact checked content is summarized. In some embodiments,content is generated by locating and utilizing information from opposingperspectives. For example, content is generated using two sources, oneConservative and one Liberal, and the information retrieved from thesources is incorporated with templates to generate a story. In someembodiments, source information is classified (e.g.,Conservative/Liberal) manually or automatically to determine the sourcesprovide opposing perspectives. Furthering the example, each of thesources is face checked, and more content is retrieved and utilized fromthe source with the higher factual accuracy rating and/or validityrating. An exemplary method of generating content with opposingperspectives includes: analyzing a first source content, analyzing asecond source content, fact checking the first source content and thesecond source content, retrieving data based on the analysis (e.g., onlyfactually accurate content), generating text/audio/images using atemplate, inputting the retrieved data in the template anddisplaying/transmitting the generated/retrieved content.

In some embodiments, the content generated is based on a keyword orphrase detected in monitored content. For example, a user is watching anews program, and a reference is made to an obscure topic. An article isautomatically generated about the topic and is presented/sent to theuser's smart phone. In some embodiments, the source of the detectedkeyword and/or user information (e.g., preferences, politicalaffiliation, other information) is taken into account when generatingthe article. For example, if the source is Conservative, then thearticle is generated using Conservative content, or opposing content isgenerated using Liberal content. In some embodiments, the generatedcontent includes multiple perspectives regarding the detectedkeyword/phrase.

FIG. 6 illustrates a flowchart of a method of news aggregation,summarization and fact checking according to some embodiments. In thestep 600, information is gathered or collected. For example, a webcrawler searches for and determines information such as news articles,images, video, social networking postings, and/or any other information.In another example, videos/media are downloaded and stored. In someembodiments, content with opposing perspectives is aggregated. Forexample, content is collected and classified (e.g., Conservative orLiberal), and then content from each perspective is aggregated/utilized.The content is able to be classified in any manner (e.g., manually orautomatically). For example, manually classifying content includes usersselecting a classification for content, or a media company classifyingcontent, and the classifications are stored. In another example,automatically classifying content based on perspective is able to bebased on matching an author/source/distributor of the content with adatabase storing names and perspectives, counting the number of keywordsin the content, matching keywords in the title with topic keywordsstored in a database, and/or any other manner. A database is able tostore the classified content as pairs of opposing perspectives (e.g.,Liberal/Conservative). If a content is determined to be from a firstperspective or classification, then content in the other classificationof the pair is considered to be from the opposing perspective (e.g.,content is classified as Liberal, so content classified as Conservativeis considered to be an opposing perspective). In the step 602, thegathered information is processed such as formatting, translating,organizing and/or any other processing to generate aggregatedinformation. For example, the processing includes organizing the contentin a user-friendly format specific to the user's device based on thedevice's capabilities or features or based on the user's preferences. Inanther example, the aggregated information is organized in a list/tileform and/or is displayed. In some embodiments, the aggregatedinformation is provided in a news feed. In the step 604, the aggregatedinformation is summarized (e.g., using lexical chaining) as describedherein. In some embodiments, each separate piece of information (e.g.,article) is summarized separately. In some embodiments, the aggregatedinformation is summarized as a whole (e.g., 1 summary is generated). Inthe step 606, the summarized information is fact checked by comparingthe summarized information with source information. In the step 608, theaggregated information and/or summarized information is provided withthe fact check results. In some embodiments, fewer or additional stepsare implemented. For example, in addition to fact checking thesummarized information, the entire aggregated information (or a largerportion than the summary) is fact checked as well. In some embodiments,the order of the steps is modified. For example, in some embodiments,the aggregated information is fact checked, and then the fact checkedinformation is summarized (e.g., only content, such as an article, witha factual accuracy rating above a threshold is summarized and/or onlyspecific elements, such as sentences, that are determined to be aspecific characterization, such as only factually accurate or onlyfactually inaccurate, are summarized).

In some embodiments, a modified implementation is utilized such asaggregating information and fact checking the aggregated informationwithout summarization. The fact checking is implemented such thataggregated content is not displayed if the factual inaccuracy count ofthe content is above a threshold (e.g., count number of factuallyinaccurate phrases, and if above 10, then do not display the entirecontent (e.g., article) or the threshold is based on percentages—morethan 10% of phrases or sentences are inaccurate, then the article is notdisplayed). For example, if 10 articles are found and aggregated, and 3are determined to be factually inaccurate, then only 7 are displayed forthe user. The threshold is able to be based on anyclassification—accurate, inaccurate, biased, questionable, and/or anyother classification. In some embodiments, instead of not displayingaggregated content, the content is displayed but the fact check resultsare displayed with the content (e.g., article is displayed but withhighlighting and description of inaccurate content). In someembodiments, the fact checking occurs before the aggregation. Forexample, if content is fact checked, and the content has too manyinaccuracies or too much bias, then the content is not collected andstored. In another example, the content is aggregated after being factchecked, but the fact check results are stored with the content forfuture display.

FIG. 7 illustrates a flowchart of a method of fact checking utilizedwith news aggregation and news generation according to some embodiments.In the step 700, narrative information is generated by analyzing one ormore data sources including extracting data from the data sources andorganizing data from the data sources into a narrative. In someembodiments, the data includes only factual information, and in someembodiments, the data only includes factually accurate information. Insome embodiments, the data includes any type of information. In someembodiments, the data sources include a single type of data source suchas a database, and in some embodiments, the data sources includemultiple types of data sources such as a database, web pages, televisioncontent, and/or any other content. In the step 702, aggregateinformation is generated by aggregating data from one or more datasources. In some embodiments, the data sources for aggregating are thesame data sources as the data sources for generating narrative content.In some embodiments, the data sources are different or there is someoverlap of the data sources. Aggregating the data is able to beperformed in any manner such as crawling for content (e.g., newsstories) and organizing the content in a user-friendly manner. The datais able to be crawled for based on author, distributor, network,website, source, keyword, and/or any other content. When data is found,the data or identification information of the data is stored. Forexample, a link to a web page is stored in a database. In anotherexample, the text (and other content) of a web page is stored. Inanother example, a transcript of audio is stored. In the step 704,target information is analyzed. Analyzing is able to include any form ofanalysis such as parsing the information into sentences/phrases,locating and filtering out redundant information, locating matching orfrequently used words/phrases, and/or any other analysis. In someembodiments, the target information includes the narrative information,the aggregate information and/or additional information. For example, anarticle about a user is generated automatically, aggregate informationis gathered regarding the user, and a user has made a social networkingpost—all of these are analyzed. In another example, an article about theeconomy is automatically generated based on statistics and datagathered, articles that discuss the economy are also aggregated, and avideo is discussing the economy—all of these are analyzed. In someembodiments, the target information does not include the narrativeinformation and/or the aggregate information. For example, the targetinformation only includes a video the user is currently watching. Inanother example, the target information includes a webpage the user iscurrently reading and aggregated information related to the webpage. Inthe step 706, the target information is summarized to generate a summaryof the target information. For example, the summary includes 5 sentencesextracted from the original content. In the step 708, the summary of thetarget information is fact checked by comparing the summary of thetarget information with source information to generate a result. In thestep 710, a status of the target information is provided in real-timebased on the result of the comparison of the summary of the targetinformation with the source information. In some embodiments, fewer oradditional steps are implemented. For example, in addition to or insteadof fact checking the summarized information; the entire aggregatedinformation (or a larger portion than the summary), the generatedinformation and/or any target information is fact checked. In someembodiments, the order of the steps is modified.

In some embodiments, content (e.g., articles) is retrieved and/orpresented based on its factual accuracy. For example, if two articlesare written about a news story (e.g., same topic and/or date and/ornumber of matching keywords above a threshold), the two articles arefact checked, and the more accurate one is presented. In someembodiments, after the two articles are fact checked, only the moreaccurate one is summarized, and the summary is presented. In someembodiments, the articles are summarized first, and then the summariesare fact checked.

In some embodiments, contributors (e.g., executives, entrepreneurs,investors) are able to provide information to a site, and theinformation is fact checked automatically. For example, CEO X providescommentary on a revolutionary technology, but before or after hiscommentary is posted to the site, it is fact checked as describedherein. In some embodiments, the commentary is summarized before orafter it is fact checked, or the commentary is summarized instead ofbeing fact checked. In some embodiments, content is generated. Forexample, content is generated related to the contributor/contributor'scontent. In some embodiments, the generated content supplements thecontributor's content. In some embodiments, content is aggregatedrelated to the contributor. For example, articles about the contributor(e.g., detected by searching for contributor's name), articles writtenby the contributor, videos of the contributor (e.g., detected by facialrecognition), and/or any other content are aggregated.

In some embodiments, content on a same topic is compared when generatinga summary and/or new content and/or aggregating content. In someembodiments, the content is from different perspectives or opposingperspectives. For example, content from a first perspective is comparedwith content from a second perspective. Furthering the example, anarticle about “taxes” written by a Conservative is compared with anarticle about “taxes” written by a Liberal. The comparison of thecontent is able to be performed in any manner. For example, utilizinglexical chaining, both articles are summarized, and the summaries areprovided/displayed together (e.g., next to each other, one after theother, one above the other). In some embodiments, the summaries of eacharticle are interwoven. For example, the first sentence from the firstsummary is provided, followed by the first sentence from the secondsummary, followed by the second sentence from the first summary, and soon. In some embodiments, the sentences from each summary are organizedso that they address the same specific topic in the article (e.g., basedon keywords). In some embodiments, the articles are fact checked beforeand/or after the summaries are generated. For example, the articles arefact checked, and only sentences that are determined to be factuallyaccurate are utilized in generating the summary. Furthering the example,words in sentences that are determined to be factually inaccurate arenot counted/used when performing lexical chaining.

FIG. 8 illustrates a flowchart of a method of utilizing multiple itemsof content to generate a summary or new content according to someembodiments. In the step 800, two or more items of content (e.g.,articles) are located. In some embodiments, locating includesdetermining the topic of the content and/or the perspective of thecontent. Determining the topic is able to be performed in any mannersuch as counting the number of matching keywords in the content,matching keywords in the title with topic keywords stored in a database,and/or any other manner. Determining the perspective of the content isable to be performed in any manner such as matching anauthor/source/distributor of the content with a database storing namesand perspectives, counting the number of keywords in the content,matching keywords in the title with topic keywords stored in a database,and/or any other manner. For example, if an article is written by aknown conservative, it can be assumed that the article is written from aconservative perspective. A database storingauthors/commentators/broadcasters/bloggers/politicians/other entities,sites, and/or other identifying information with the perspectiveinformation is able to be utilized. For example, a database stores namesof authors and their political leaning/perspective. Other information isable to be utilized to determine an author's perspective, such aspersonal information, background information, and/or social networkinginformation. For example, if a very wealthy person writes an article onthe benefits of lowering taxes, it may be assumed that the person is notwriting from a poor person's perspective. In the step 802, the contentis analyzed. Analyzing is able to include processing such as parsing,formatting and/or any other analysis. In the step 804, the content issummarized. In the step 806, the content and/or summary are factchecked. For example, if the content is fact checked, modified contentis able to be generated where any content that is determined to befactually inaccurate or another specified classification is not includedin the modified content. For example, a content has 100 sentences, 15 ofthe sentences are determined to be factually inaccurate, so the modifiedcontent is generated with by deleting those 15 sentences such that only85 sentences remain. In the step 808, an informative content with two ormore perspectives is provided by combining the summaries and/or modifiedcontent. In some embodiments, fewer or additional steps are implemented.For example, the steps of fact checking and/or summarizing are omitted,and the two or more contents are combined. In some embodiments, theorder of the steps is modified. For example, the content is fact checkedbefore being summarized, and the summarizing takes into account the factchecking results.

FIG. 9 illustrates a flowchart of a method of utilizing fact checking togenerate content according to some embodiments. In the step 900, contentis analyzed. Analyzing is able to include processing such as parsingand/or any other analysis. For example, the content is analyzed anddetermined to be related. For example, using a keyword-based analysis,summary comparison analysis, time/date analysis, topic analysis, and/orany other analysis, it is determined that two or more articles arerelated. Furthering the example, it is determined based on keywordcomparison, topic analysis and the dates of the articles, that twoarticles are related to the effect of tax cuts on employment. In someembodiments, articles with opposing perspectives are located andanalyzed, or several articles with same perspective on each side of anargument are utilized. For example, an article with a liberalperspective on tax cuts and an article with a conservative perspectiveon tax cuts are analyzed. Determining that articles or other contenthave opposing perspectives is able to be performed in any manner such asmanually classifying and selecting content in opposing sides (e.g.,liberal/conservative), by comparing authors/sources of articles (e.g.,Fox News/MSNBC or conservative author/liberal author), based on specifickeyword detection (e.g., pro-choice/pro-life), and/or any other manner.In the step 902, the content is fact checked as described herein. Forexample, several articles are fact checked. In the step 904, theinformation (e.g., sentences or phrases) that is determined to befactually accurate is retained, and the other information is discarded.In some embodiments, only the information with the highest confidencescore (e.g., top 10 sentences) is retained. The confidence score is ableto be generated in any manner; for example, a confidence score is basedon the number of sources that agree or disagree with the information—10sources agree/match with the information and 0 disagree=100% confidencescore. In the step 906, based on the fact checking, a joint summary ornew content is generated. For example, a data structure stores the top 5factually accurate sentences from each document, and an output is the 5sentences from each, interwoven to generate content. In someembodiments, the content is color-coded and/or another distinguishingfeature is utilized. For example, all sentences from Article 1 are ingreen, and all sentences from Article 2 are in red. In some embodiments,the content is interwoven in chronological order based on the originalcontent (e.g., even though Sentence 1 has the highest confidence score,since it appeared at the beginning of an article, it is placed first inthe new content). In some embodiments, the 5 sentences from each arekept separate but placed proximate to the other content, so that a useris able to read the sentences from each article separately. For example,5 sentences from Article 1 are stored/presented, and immediately beloware 5 sentences from Article 2. In some embodiments, the sentences fromeach article are matched based on keywords and/or any other matching, sothat relevant sentences are included/displayed together. In someembodiments, the factually accurate content is utilized with orincorporated in one or more templates to generate new content. Forexample, a template includes the following text: “The topic is*keyword*, and from one perspective: *FC1* but from a differentperspective *FC2* . . . ” where FC1 and FC2 are selected sentences fromthe content that are determined to be factually accurate. In someembodiments, fewer or additional steps are implemented. For example, insome embodiments, the content is located automatically by searching forkeywords and/or any other method of locating content. In anotherexample, the content is summarized as described herein and the summariesare merged. In some embodiments, the order of the steps is modified.

FIG. 10 illustrates a flowchart of a method of utilizing fact checkingfor summarization according to some embodiments. In the step 1000,content is analyzed. Analyzing is able to include processing such asparsing and/or any other analysis. In the step 1002, the content is factchecked as described herein. In the step 1004, the information (e.g.,sentences or phrases) that is determined to be factually accurate isretained, and the other information is discarded. For example, ifinformation is determined to be questionable or inaccurate, then it isdiscarded. In some embodiments, only the information with the highestconfidence score (e.g., top 10 sentences) is retained. In someembodiments, only factually inaccurate information is discarded suchthat factually accurate and questionable information are retained. Insome embodiments, information determined to be biased is discarded, andin some embodiments, biased information is retained. Information is ableto be determined as biased in any manner, such as comparing theinformation with a bias database (e.g., storing keywords that indicatebias), crowdsourcing, manually labeling items as biased or not, and/orany other manner. In some embodiments, other characterizations ofcontent (e.g., unverified) are retained or discarded. In the step 1006,summarization analysis (e.g., lexical chaining) is performed. In someembodiments, the summarization analysis is performed only on theretained information. For example, if 75 out of 100 sentences areretained as being factually accurate, lexical chaining is performed onthose to generate a summary. Thus, the summary is generated withoutusing the 25 non-factually accurate sentences. In some embodiments, thesummarization analysis is performed on the entire content. Then, acomparison is made between the information determined from thesummarization analysis and the fact checking. For example, if Sentence 1is determined to be factually accurate and relevant to a summary basedon lexical chaining, then Sentence 1 is utilized when generating asummary; however, if Sentence 2 is determined to be factually inaccurateand relevant to a summary based on lexical chaining, then Sentence 2 isnot utilized when generating a summary, or if Sentence 3 is determinedto be factually accurate but not relevant to a summary based on lexicalchaining, then Sentence 3 is not utilized when generating a summary. Insome embodiments, the summarization analysis is performed on theretained information and the entire content. For example, if thesummarization analysis of the retained information results in asufficient summary (e.g., above a number of sentences threshold or anyother qualification), then the summarization analysis is not performedon the entire content, but if the summarization analysis of the retainedinformation does not have a sufficient result, then the summarizationanalysis of the entire content is performed, and a comparison of thatsummarization analysis with the fact checking results is performed togenerate a summary. In the step 1008, a summary is generated/provided.For example, the summary is sent to a user device and/or displayed on ascreen. In some embodiments, fewer or additional steps are implemented.For example, in some embodiments, the step of summary analysis is notperformed, and the summary is simply the content remaining afterdiscarding the non-factually accurate information or inaccurateinformation. In some embodiments, the order of the steps is modified.

In some embodiments, a summary is provided as a list/tiles/GUI ofsentences/phrases that are selectable (e.g., clickable), where selectingan item from the list causes the full content (e.g., article) to bedisplayed at the location of the selected sentence, with the sentencehighlighted. For example, a user is presented a summary of a speech,where the summary is 5 sentences from the speech determined usinglexical chaining. The 5 sentences are displayed as separate clickableitems. When the user clicks one of the sentences, the user is taken tothe sentence in the full transcript of the speech or the point of theaudio where the sentence is said.

In some embodiments, a summary is generated based on fact checkingand/or other summarization implementations by utilizing the mostfactually accurate and/or most factually inaccurate content. Forexample, the 5 most factually inaccurate items (e.g., sentences/phrases)are retained and used for the summary. In another example, the 5 mostfactually accurate items that are associated with the top 3 lexicalchains are used for the summary. Determining the 5 most factuallyaccurate or inaccurate items is able to be performed manually orautomatically. For example, the fact checked items are able to be rankedbased on confidence scores, numbers of agreeing/disagreeing sourcesand/or fact checking score above/below a threshold.

In some embodiments, user responses (e.g., thumbs up/down), tweets,likes/dislikes, comments, and/or any other user input are incorporatedwhen merging, generating, aggregating, and/or summarizing content. Forexample, if an article has been re-tweeted many times, the number ofsentences from that article used when merging, summarizing, and/orgenerating is increased. Furthering the example, if an article hasreceived no comments, then a standard amount of sentences (e.g., 5sentences) is used for summarizing the article, but if the article hasreceived many likes on a social networking site, then the amount ofsentences used for summarizing the article is increased (e.g., by 2). Insome embodiments, if an article has been disliked by enough users (e.g.,above a threshold), then the article is not used when performing amerge/generation of new content. For example, if an article has received100 thumbs down, then it may not be a good article to use whenpresenting opposing perspectives, so even though it may pass othercriteria, it is not permitted to be used. Similarly, comments followingan article are analyzed and utilized. For example, if many of thecomments write, “this is a terrible article,” then the article isnegatively affected (e.g., not used).

In some embodiments, the merging, generating, aggregating, summarizingand/or fact checking automatically incorporate user interests. Userinterests are able to be used to determine which content to merge,generate, aggregate, summarize and/or fact check and/or which content toprovide/display to a user and/or how to provide/display the content. Forexample, topics are able to be determined from the user interests, andthe topics are utilized by the system to select topics for contentgeneration or content aggregation. For example, if a user is interestedin stocks, then articles about stocks are automatically generated forthat user as opposed to articles about reality stars. In anotherexample, finance articles are aggregated using detected keywords in thearticles for a user who is interested in stocks. In another example,when summarizing, keywords based on a user's interests are used indetermining which sentences to retain for the summary. Furthering theexample, lexical chains are analyzed to determine if any user interestkeywords are within any lexical chain, and if so, that chain isconsidered strong or is given added strength. User interests are able tobe determined in any manner, such as clicking “interested” aftervisiting a website, or filling out a survey, or not clickingdisinterested after visiting a website where classification/topic of thesite is automatically accepted by visiting, via social networking (e.g.,analyzing social networking content for keywords/topics), by “liking”content, by sending a tweet with a hashtag or other communication withthe topic, by selecting content (e.g., from list of selectable sources),using another social media forum (e.g., items/photos pinned on Pinterestand videos liked on Youtube indicate interests of those users), and/orany other implementation. In some embodiments, a topic is considered tobe of interest to a user if the topic is associated with anorganization/entity that the user has “liked” (or a similarimplementation), where associated means approved by, written by,affiliated with, or another similar meaning. In some embodiments, atopic of a site becomes an interested topic if a user uses or visits thesite. In some embodiments, a site is one of interest if a user uses orvisits the site while signed/logged in (e.g., signed in to Facebook® orGoogle+®). In some embodiments, the user must be logged into a specificsocial networking system, and in some embodiments, the user is able tobe logged into any social networking system or a specific set of socialnetworking systems. In some embodiments, the sites are limited to aspecific method of indicating interest such as only sites visited whilelogged in. In some embodiments, a site topic is of interest if the siteis recommended to the user (e.g., by a contact) (even if the user doesnot visit/review the site), unless or until the user rejects/disapprovesof the site. In some embodiments, sites/topics are suggested to a userfor a user to accept or reject based on contacts of the user and/orcharacteristics of the user (e.g., location, political affiliation, job,salary, organizations, recently watched programs, sites visited). Insome embodiments, the contacts are limited to n-level contacts (e.g.,friends of friends but not friends of friends of friends). In someembodiments, users are able to indicate disinterest in topics.

In some embodiments, content/data for aggregation, summarization,generation, fact checking and/or merging is able to be retrieved frommany different sources such as databases, social networking sites/posts(e.g., tweets, Facebook® postings), newsfeeds, television, radio, gamingcommunications, messaging, and/or any other communication/information.

In some embodiments, summarization (e.g., lexical chaining) is utilizedwith multiple items of content. For example, Fox News, CNN and MSNBC allhave 1 story about a recent event. To provide a complete summary, allthree articles are analyzed and utilized to generate a summary. Forexample, the 3 articles are merged into one complete article, andlexical chaining is performed on the merged article. Then, based on thelexical chaining of the merged article, a summary is generated. In someembodiments, the relative position of each sentence is maintained whenmerging the articles. For example, the first sentence of each article isgiven position 1, so as not to change the positioning of the sentencesafter the merger, since sentences closer to the beginning may be givenmore weight than sentences in the middle of the article. In someembodiments, the articles are interwoven when merged such that the firstsentence from each are together, the second sentence from each aretogether and so on. In some embodiments, dates, headlines, headings,and/or other information is utilized in determining which articles tosummarize together. In some embodiments, the multiple items of contentare from the same source (e.g., network) such as the transcript from 5CNN shows. In some embodiments, the multiple items are different kindsof content (e.g., article, transcript, video log). In some embodiments,the multiple items are from different sources (e.g., Fox News, CNN,MSNBC). In some embodiments, the summarization is used to determine if atopic is worth fact checking. For example, if the lexical chainingprovides poor results, it may indicate that the topic is not worth factchecking, and fact checking resources are reserved for another topic.

In some embodiments, links or any other access to a full story areprovided with a summary and/or merged story. For example, if twoarticles are merged into one, links to each of the articles are providedfor the user to access the original articles.

In some embodiments, when multiple items of content are generated,summarized and/or merged, the items are fact checked before beingsummarized/merged, and the fact checking results affect thesummarization/merger. For example, the items are given a factualaccuracy rating based on the fact checking. Furthering the example, ifan article has 1000 pieces of fact checkable content (e.g., phrases),and 100 pieces are determined to be factually inaccurate, the factualaccuracy rating is 90.00%. The factual accuracy rating is able to bedetermined in any manner. The factual accuracy ratings of the items areable to be compared. For example, a first article receives a 95.00%accuracy rating, and a second article receives a 92.00% accuracy rating.Since the first article has a higher rating, it receives preferentialtreatment. For example, the summary of the first article is placed firstor in larger print. In another example, when merging the two items, 7sentences are retrieved from the first article, and only 5 items areretrieved from the second article. Similarly, when aggregating content,the higher factually accurate content is placed first or above the loweraccuracy content. For example, if 10 articles are aggregated, the mostprominent article is the one with the highest factual accuracy, and soon until the article with the lowest factual accuracy. In someembodiments, if an article (or other content) has a factual accuracyrating below a threshold, the article is not aggregated, summarized,merged, or used for story generation.

In some embodiments, an entity including, but not limited to, a speaker,author, news sources (e.g., CNN, Fox News), user, or another entity(e.g., corporation) has a validity rating that is included with thedistribution of information from him/it. The validity rating is able tobe generating automatically, manually or a combination thereof. Thevalidity rating is able to be based on fact checking results of commentsmade by an entity and/or any other information. For example, if a personhas a web page, and 100% of the web page is factually accurate, then theuser is given a 10 (on a scale of 1 to 10) for a validity rating. Inanother example, a user tweets often, and half of the tweets arefactually accurate and half are inaccurate, the user is given a 5. Forexample, the validity rating is a percent divided by 10, so 100%=10 and50%=5. In another example, a journalist's articles, tweets, interviews,and/or any other content is fact checked to determine a validity rating.The validity rating is able to be calculated in any manner. In additionto fact checking information by an entity, items such as controversies,bias, and/or any other relevant information is able to be used incalculating a validity rating. The severity of the information ormisinformation is also able to be factored in when rating a person orentity. Additionally, the subject of the information or misinformationis also able to be taken into account in terms of severity. In someembodiments, an independent agency calculates a validity rating and/ordetermines what is major and what is minor. In some embodiments,individual users are able to indicate what is important to them and whatis not. In some embodiments, another implementation of determining whatis major, minor and in between is implemented. The context of thesituation/statement is also able to be taken into account. In someembodiments, entities are able to improve their validity rating if theyapologize for or correct a mistake, although measures are able to betaken to prevent abuses of apologies. In some embodiments, in additionto or instead of a validity rating, an entity is able to include anotherrating, including, but not limited to, a comedic rating or a politicalrating. In some embodiments, an entity includes a classificationincluding, but not limited to, political, comedy or opinion. Additionalfactors usable for the calculation of validity ratings include, but arenot limited to the number of lies, misstatements, truthful statements,hypocritical statements or actions, questionable statements, spin,and/or any other characterizations.

In some embodiments, the validity rating is taken into account whencontent is merged, summarized, generated, aggregated, and/or factchecked. For example, a first article is written by a journalist with avalidity rating of 9 out of 10, and a second article is written by ajournalist with a validity rating of 7 out of 10. Since the journalistof the first article has a higher rating, that article receivespreferential treatment. For example, the summary of the first article isplaced first or in larger print. In another example, when merging thetwo items, 7 sentences are retrieved from the first article, and only 5items are retrieved from the second article. Similarly, when aggregatingcontent, the content of the author with the higher validity rating isplaced first or above the lower validity rated authors. For example, if10 articles are aggregated, the most prominent article is the one by theauthor with the highest validity rating, and so on until the articlefrom the author with the lowest validity rating. In some embodiments, ifan author/entity has a validity rating below a threshold, the author'scontent is not aggregated, summarized, merged, or used for storygeneration. In some embodiments, if an entity's validity rating is abovea first threshold, then x (e.g., 7) sentences from the content by theentity are used, if the entity's validity rating is equal to or belowthe first threshold but above a second threshold, then y (e.g., 5)sentences from the content by the entity are used, and if the entity'svalidity rating is equal to or below the second threshold, then z (e.g.,3) sentences or no sentences from the content by the entity are usedwhen merging, generating, and/or summarizing content. Any number ofthresholds are able to be utilized. In some embodiments, fact checkingis implemented differently based on an entity's validity rating. Forexample, if a journalist's validity rating is below a threshold, hiscontent is fact checked by 2 different fact checking implementations,and if either implementation determines content to be inaccurate, thecontent is labeled as such or is negatively affected or his content istreated differently. In another example, content determined to bequestionable or unverified of a journalist with a validity rating abovea threshold is utilized (e.g., in generating, aggregating, summarizing),but content determined to be questionable or unverified of a journalistwith a validity rating equal to or below a threshold is not used.

In some embodiments, a satisfaction rating is generated and/or utilizedfor an entity. For example, based on a survey of employees, a survey ofcustomers, and/or analysis of social networking information, acorporation receives a satisfaction rating of how satisfied people arewith the corporation. In another example, a journalist receives asatisfaction rating based on analysis of the stories provided by thejournalist. The determination of the satisfaction rating is able to begenerating automatically, manually or a combination thereof. Forexample, the comments section of an article is analyzed by searching forkeywords, analyzing the thumbs up/down of comments, analyzing a ratingof the article, analyzing the number of views/clicks of an article,analyzing the number of times an article is forwarded/shared/re-tweetedto other users, analyzing the duration of user views, and/or any otheranalysis, and based on this analysis a satisfaction rating is able to begenerated. Furthering the example, if the comments describe the articleas “poorly written” that would negatively affect the satisfaction ratingof the author. As an example, Journalist X writes many articles, and hisarticles are posted online, and using a thumbs up or down clickablesystem, the articles receive many negative responses (only 3 out of 10responses are thumbs up resulting in a base score of 3); additionally,the comments sections frequently include negative comments based onkeyword analysis and lexical chaining of the comments (which is a −1added to the base score), so Journalist X receives a rating of 2 out of10. Any calculations/equations are able to be used to generate asatisfaction rating. In some embodiments, a user is able to specify thathe likes or dislikes a specific author/reporter/media outlet/otherentity. In some embodiments, by specifying that the user likes a entity,the satisfaction rating is set to the highest value regardless of otheranalysis. Similarly, if a user dislikes an entity, the satisfactionrating is set to the lowest value regardless of other analysis. Thesatisfaction rating is able to be utilized when determining if anarticle is used when generating, merging, summarizing, aggregating, factchecking, and/or any other operation on content. For example, ifJournalist X has received a satisfaction rating of 2 out of 10, and athreshold is set at 7 of 10, where articles with a rating below 7 areexcluded from being used when aggregating content, then Journalist X'sarticles are excluded when aggregating. In some embodiments, fact checkresults are utilized in generating a satisfaction rating. For example,if an author receives a 7 out of 10 based on the other satisfactionrating analysis, but the articles are fact checked and determined to bevery factually inaccurate, the score of 7 is reduced to 5.

In some embodiments, a single fact check result is provided for anentire article/show/content. For example, only the most factuallyinaccurate (e.g., based on a factual accuracy score/rating) phrase orsentence is provided with analysis or commentary on its factualinaccuracy. In another example, only the factually inaccurate contentwith the highest confidence score is provided. In another example, onlythe factually inaccurate phrase in the strongest lexical chain ispresented. In another example, only the fact check result (e.g.,factually accurate, inaccurate, questionable) of a phrase in thestrongest lexical chain is presented.

In some embodiments, the fact checking using summarization (e.g.,lexical chaining) is utilized with re-runs, replays, second broadcasts,and/or any other additional presentations of the content. For example, avideo is played which is fact checked using summarization, and thenfuture displays of the video include the fact check results based on theprevious fact checking so that fact checking does not need to berepeated, and the fact check results are displayed in real-time.

In some embodiments, content is generated, summarized, aggregated,and/or fact checked using social networking/media content (e.g., tweets,Facebook® postings). For example, a news article is generated regardinga current international conflict, and in addition aggregating newsarticles, tweets related to the conflict are aggregated as well. In someembodiments, the articles and/or the tweets are fact checked before orafter being aggregated. For example, if a tweet has a factual accuracyrating below a threshold, then the tweet is not included in theaggregation. Similarly, if a tweet has a factual accuracy rating below athreshold, the tweet is not utilized when generating content. In someembodiments, only part of the content is fact checked (e.g., only tweetsare fact checked, but the articles are not. In some embodiments,validity ratings and/or satisfaction ratings are utilized to determineif content is utilized when generating, summarizing, and/or aggregating.For example, a tweet of a user with a validity rating below a thresholdis not utilized.

In some embodiments, a story is generated solely utilizing socialnetworking/media content. For example, tweets are analyzed (e.g., tofind tweets regarding a similar topic—for example, based on the samehashtag), the tweets are fact checked, the tweets with a factualaccuracy rating and/or validity rating below a threshold are discarded,and summarization (e.g., lexical chaining) is utilized to extractrelevant content from the tweets to generate a consolidated/summarizedpiece of content. In some embodiments, the multiple tweets areconsolidated into a single tweet or a tweet with a link to a largersummary or another type of media (e.g., Facebook® post, web page).Similarly, aggregation is able to be utilized using only socialnetworking content. In some embodiments, a larger percentage of contentis generated/aggregated from social networking information. In someembodiments, a larger percentage of the resulting content is socialnetworking information. For example, 51% of generated content is or isfrom social networking content. Any percentage of generated content isor is from social networking information (e.g., 51%, 75%, 90%, 95%,99%).

In some embodiments, for breaking news that is difficult to verify,items are labeled as unverified until they are verified by sufficientresources. For example, next to a headline regarding a breaking newsstory, an icon symbolizing “unverified” is displayed. Additionally, thenumber of re-posts is able to be indicated. For example, the number oftimes that the story has been re-tweeted is indicated. The number ofsources of information is also able to be indicated. For example, if 4major networks are reporting a story, that may indicate more accuracy tothe story than if only 1 network is reporting the story. Determining whois reporting a story and/or how many sources are reporting a story isable to be performed in any manner, such as using keyword analysis bymonitoring and comparing transcripts of sources. In some embodiments,social networking information is also monitored to determine if thereare any eye witnesses and/or images/videos which verify the breakingnews. In some embodiments, stories are compared to determine if they arethe same or similar, and the source of each story is determined. In someembodiments, stories are compared to determine if they conflict or ifthey agree. For example, it is determined that Fox News and CNN are bothreporting that X occurred, so it is indicated that two sources agree. Inanother example, it is determined that Fox New is reporting that Xoccurred, and CNN is reporting that Y occurred, so it is indicated thatthere are conflicting reports. In some embodiments, the sources areindicated.

In some embodiments, the generation, aggregation, summarization and/orfact checking content utilizes language differences. For example, whengenerating content, articles from different languages are searched forand utilized. Furthering the example, to obtain different perspectives,news from Russia about Topic Z is likely going to have a differentperspective than news from the U.S. Determining content is a differentlanguage is able to be performed in any manner such as by detectingwords specific to a language (e.g., comparing words with differentlanguage dictionaries), detecting characters specific to a language,utilizing sources which provide content in different languages (e.g.,CNN provides content in English, and Univision provides content inSpanish), based on metadata indicating the language, and/or any otherimplementation. Then, for example, an article in each language isutilized and merged using summarization as described herein. The newarticle includes information from the English article and the Russianarticle. When aggregating, generating, summarizing, and/or factchecking, the content is able to be automatically translated to thelanguage of the viewer of the content. For example, the content inRussian is translated to English for an English user, so that when thecontent is displayed, the English user is able to read it. In someembodiments, the information is fact checked using sources originally inthe same language. For example, a Russian article is fact checked usingonly Russian sources. In some embodiments, the information is factchecked using sources in another language (e.g., if two articles, oneEnglish and one Russian, are being used to generate another article,then the English article is fact checked using Russian sources, and theRussian article is fact checked using English sources). In someembodiments, the information is fact checked using sources in both/alllanguages.

FIG. 11 illustrates a flowchart of a method of utilizing languagedifferences in generating, aggregating, summarizing and/or fact checkingaccording to some embodiments. In the step 1100, content is searched forincluding determining content is in different languages. In someembodiments, searching for content includes searching for keywords toensure the content is the same topic, where the keywords arepaired/grouped by language (e.g., airplane and avión are paired). Forexample, a data structure matches a word in a first language with a wordin a second language. In the step 1102, the content is summarized asdescribed herein. In the step 1104, the summaries are merged asdescribed herein. For example, a first sentence of the English contentsummary is used first, and a first sentence of the Russian contentsummary is used second, and so on. In the step 1106, the multi-lingualcontent is translated to the user's language and displayed. For example,for an English-speaking user, only the Russian content or non-Englishcontent is translated. In some embodiments, fewer or additional stepsare implemented. For example, the content is fact checked before beingsummarized, and only factually accurate content is used whensummarizing. In some embodiments, the order of the steps is modified.

In some embodiments, advertising is generated based on lexical chaining.For example, when content is aggregated, summarized, and/or generated,the keywords of a lexical chain are analyzed to generate anadvertisement which is able to be displayed with the content. Furtheringthe example, an article is generated about taxes, so an advertisementfor tax preparation software is presented. For example, a keyword fromlexical chaining is matched with a data structure containing keywordsand advertisements that correspond to the keywords. In some embodiments,when several articles are utilized (e.g., merged), a keyword from thelexical chain from each article is used to generate the advertisement.For example, a first article is about golf, and a second article isabout taxes, so an advertisement about money management is generated andprovided.

In some embodiments, when generating an article, the content used togenerate the article includes related topics or unrelated topics. Forexample, two articles with opposing views on cutting taxes are used togenerate content, and the two articles are related to the same topic oftaxes. In another example, an article about golf is merged with anarticle about taxes to provide content that involves two interests of auser.

In some embodiments, the optimized fact checking is utilized withaugmented reality devices (e.g., glasses) and/or augmented realityinformation. Any of the methods described herein are able to be utilizedwith the augmented reality device such as generating, aggregating,summarizing, and/or fact checking. For example, a story is generatedand/or summarized and presented on the user's augmented reality device.In some embodiments, the story is generated using/based on contentacquired using the augmented reality device. For example, the user'saugmented reality device detects Restaurant X, and instead of or inaddition to providing a list of Yelp reviews, an automatically generatedsummary of the Yelp reviews is provided to the user on the augmentedreality device. In another example, an augmented reality device detectsa corporation's logo, and provides the user with a summary of thecorporation, generates a news story about the company's finances, and/oraggregates recent news articles about the company, any of which is factchecked or not. In some embodiments, an augmented reality device detectscontent and provides a summary or fact checked summary. In someembodiments, an augmented reality device detects a politician or otherperson (e.g., using face/voice recognition), and generates a descriptionof the politician including an opposing perspective of the politician'sviews. In some embodiments, a restaurant name is detected, and articles,social networking information (e.g., tweets), and/or any other contentrelated to the restaurant is aggregated and/or fact checked.

An augmented reality device is able to be any augmented reality device(e.g., Google Glass®). For example, the augmented reality deviceincludes a camera and a display which are able to be worn on a user'sface. The augmented reality device also includes a processor and memoryfor processing and storing information such as applications and acquiredinformation. The applications are able to perform functions such as facerecognition, content recognition (e.g., text recognition), imagerecognition, and use acquired information with the generating,aggregating, summarizing and/or fact checking implementations. Forexample, the camera of the augmented reality device acquires the name ofa restaurant by text/image recognition of the data received by thecamera, then articles are found using the name of the restaurant, thearticles are summarized and fact checked, and the summaries with factcheck results are displayed on the display of the augmented realitydevice.

In some embodiments, headlines are aggregated and fact checked insteadof or in addition to entire articles, and links for the full article areprovided.

In some embodiments, the generation, aggregation, summarization, and/orfact checking are utilized with a search engine. For example, when auser searches for a search string, a summarization of recent articlesrelated to the search string is automatically generated. Furthering theexample, articles/web pages/content are located based on the searchstring, then a summarization implementation is utilized to consolidatethe articles into a summary which is displayed in addition to the searchresults. In another example, a most recent article/content for thesearch string is found and automatically fact checked and summarizedwhich is presented with the search results.

In some embodiments, the generation, aggregation, summarization, and/orfact checking utilize email, text messages, social media/networking topost results. For example, aggregated articles that have been factchecked are sent to a user's email periodically.

In some embodiments, any of the steps/methods described herein such asgenerating, aggregating, summarizing, fact checking, and/or determiningopposing perspectives, are able to be implemented utilizing cloudcomputing. For example, the fact checking is implemented in a cloudcomputing device and results are sent to user devices. Furthering theexample, user devices monitor information, the information is sent tothe cloud where it is processed and/or fact checked, and then resultsare sent to the user devices. In another example, a story is generated,content is aggregated, the aggregated content is summarized and factchecked, all in the cloud, and then content and/or results aredistributed to user devices.

The methods described herein are able to be implemented separately ortogether. For example, content is able to be generated, aggregated,summarized and/or fact checked. Content is able to be fact checked thengenerated, aggregated and/or summarized. Content is able to begenerated, aggregated and/or summarized and then fact checked. Anycombination or permutation of the methods is able to be utilized. Anyorder of the methods is able to be utilized. Any of the methods are ableto be utilized multiple times. Any of the methods/steps are able to beperformed sequentially or in parallel. The output of any of the methodsis able to be used as input of any of the other methods. For example,content is fact checked, and then the fact check results are used togenerate a summary, and the summary is used for generating a story. Theoutput of any of the methods is able to be displayed and/or transmitted.The steps/methods are able to be implemented on separate content orcollective content. For example, each separate piece of aggregatedcontent is summarized separately to generate multiple summaries, oraggregated content is summarized as one to generate a single summary. Inanother example, generated content, aggregated content and other contentare summarized separately or as one. Fact checking is able to beimplemented before and/or after any step or method, and the factchecking is able to be performed on any part of the data. For example,content is aggregated, then summarized and then fact checked (e.g. theaggregated content and the summaries are fact checked, or only thesummarized aggregated data is fact checked); or content is aggregatedthen fact checked and then summarized; or content is fact checked thenaggregated and then summarized. Fact checking and/or any of the methodsare able to occur multiple times. For example, summary information isfact checked, then aggregating occurs, and then fact checking occurs onthe aggregated content.

Examples of methods include: generating content; aggregating content;summarizing content; fact checking content; generating content andaggregating content; generating content and summarizing content;generating content and fact checking content; aggregating content andgenerating content; summarizing content and generating content; factchecking content and generating content; aggregating content andsummarizing content; summarizing content and aggregating content;aggregating content and fact checking content; fact checking content andaggregating content; summarizing content and fact checking content; factchecking content and summarizing content; generating content,aggregating content, and summarizing content; generating content,aggregating content, and fact checking content; generating content,summarizing content, and fact checking content; generating content,summarizing content, and aggregating content; generating content, factchecking content, and aggregating content; generating content, factchecking content, and summarizing content; aggregating content,summarizing content, and fact checking content; aggregating content,generating content, and summarizing content; aggregating content,generating content, and fact checking content; aggregating content,summarizing content, and generating content; aggregating content, factchecking content, and generating content; aggregating content, factchecking content, and summarizing content; summarizing content,aggregating content, and fact checking content; summarizing content,fact checking content, and aggregating content; summarizing content,fact checking content, and generating content; summarizing content,generating content, and fact checking content; summarizing content,generating content, and aggregating content; summarizing content,aggregating content, and generating content; fact checking content,generating content, and aggregating content; fact checking content,generating content, and summarizing content; fact checking content,aggregating content, and generating content; fact checking content,aggregating content, and summarizing content; fact checking content,summarizing content, and generating content; fact checking content,summarizing content, and aggregating content; generating content,aggregating content, fact checking content, and summarizing content;generating content, aggregating content, summarizing content, and factchecking content; generating content, summarizing content, aggregatingcontent, and fact checking content; generating content, summarizingcontent, fact checking content, and aggregating content; generatingcontent, fact checking content, summarizing content, and aggregatingcontent; generating content, fact checking content, aggregating content,and summarizing content; aggregating content, summarizing content,generating content, and fact checking content; aggregating content,generating content, fact checking content, and summarizing content;aggregating content, generating content, summarizing content, and factchecking content; aggregating content, summarizing content, factchecking content, and generating content; aggregating content, factchecking content, summarizing content, and generating content;aggregating content, fact checking content, generating content, andsummarizing content; summarizing content, aggregating content,generating content, and fact checking content; summarizing content, factchecking content, generating content, and aggregating content;summarizing content, fact checking content, aggregating content, andgenerating content; summarizing content, generating content, aggregatingcontent, and fact checking content; summarizing content, generatingcontent, fact checking content, and aggregating content; summarizingcontent, aggregating content, fact checking content, and generatingcontent; fact checking content, generating content, summarizing content,and aggregating content; fact checking content, generating content,aggregating content, and summarizing content; fact checking content,aggregating content, summarizing content, and generating content; factchecking content, aggregating content, generating content, andsummarizing content; fact checking content, summarizing content,aggregating content, and generating content; fact checking content,summarizing content, generating content, and aggregating content. Thecontent is able to be the same content and/or different content. Forexample, if content is generated, that content is able to be summarizedand/or different content is summarized. The output of any prior methodis able to be the input of a method or not. Other methods are able to beincorporated before or after any of the steps, for example, determiningdifferent perspectives. The examples described are not meant to limitthe invention in any way.

Additional examples are described herein, but are not meant to limit theinvention in any way. For example, an article is generated utilizingdatabase information. Additionally, similar articles (based on topic)are aggregated and fact checked, and only articles with a factualaccuracy rating above a threshold are displayed to the user along withthe generated article.

For example, an article is generated utilizing web page analysis.Additionally, articles based on the web page analysis are aggregated.Only the aggregated content is fact checked.

For example, articles are aggregated and fact checked, and only thefactually accurate aggregated articles are summarized.

For example, articles are aggregated and fact checked, and only thefactually accurate aggregated articles are displayed.

For example, several web pages related to a specified topic are locatedand fact checked, and only the articles with a factual accuracy ratingabove a threshold are aggregated. Each of the aggregated articles issummarized, and the summaries with the fact checking results aredisplayed.

For example, social networking content is aggregated and fact checked,and only the social networking content that has a factual accuracyrating above a threshold is summarized.

For example, several articles related to the same topic but withopposing perspectives based on analysis of the authors are located andfact checked, but only the two most factually accurate articles aresummarized and merged to generate a new story.

For example, a story is generated, content is aggregated, and additionalinformation is located. The story, content and additional informationare summarized. The summary is fact checked, and the fact check resultsare displayed to a user.

For example, a story is generated using fact checked information. Thestory is summarized and displayed to a user.

For example, two items of content are summarized and the summaries arefact checked. The fact checked summaries are utilized to generate astory.

For example, multiple web pages are fact checked and summarized, whereonly the web pages written by entities with a validity rating above athreshold are summarized. The summaries are aggregated and displayed toa user.

For example, tweets by a CEO are fact checked and then summarized usingonly the factually accurate information of the tweets.

For example, tweets by a CEO are fact checked and then content isgenerated using only the factually accurate information of the tweets.

For example, summaries of content are aggregated and fact checked, anddisplayed in order based on their factual accuracy ratings (e.g., mostaccurate first).

For example, aggregated content is summarized individually orcollectively and fact checked.

For example, content is fact checked, and based on the fact checkresults, articles and/or summaries are generated, and/or the content isaggregated.

For example, content is aggregated and fact checked, and then a story isgenerated using the fact checked aggregated content.

For example, stories are generated, then aggregated and then factchecked.

For example, aggregated content is summarized, and the summaries areused to generate a story which is fact checked.

For example, content is summarized, the summaries are fact checked, andstories are generated using the summaries by merging only factuallyaccurate content.

For example, only factually accurate content as determined by factchecking is used to generate a story/content. In another example, onlythe factually accurate content with a confidence score above a thresholdis used.

For example, any content except for factually inaccurate content asdetermined by fact checking is used to generate a story/content.

For example, only sources with a factual accuracy rating above athreshold as determined by fact checking are used to generate astory/content.

For example, only factually inaccurate content as determined by factchecking and a template are used to generate a story/content indicatingthe content is inaccurate.

For example, only questionable content as determined by fact checkingand a template are used to generate a story/content indicating thecontent is questionable.

For example, factually accurate and factually inaccurate content asdetermined by fact checking and a template are used to generate astory/content where the template is configured to separate the factuallyaccurate content and the factually inaccurate content.

For example, all content is used to generate a story/content but thestory includes indications of factually accurate, factually inaccurate,and/or any other characterizations.

For example, only content with a factual accuracy rating above athreshold as determined by fact checking is aggregated.

For example, only content with a number of factual inaccuracies below athreshold as determined by fact checking is aggregated.

For example, only content with a factual accuracy rating equal to orbelow a threshold as determined by fact checking is aggregated and isindicated as factually inaccurate.

For example, only questionable content as determined by fact checking isaggregated and is indicated as questionable. For example, the content isnot able to be confirmed or disproved.

For example, all types of content are aggregated, and the content isindicated/classified based on fact checking.

For example, only content with a factual accuracy rating above athreshold as determined by fact checking is summarized.

For example, only content with a factual accuracy rating equal to orbelow a threshold as determined by fact checking is summarized.

For example, only factually accurate content (and/or anothercharacterization) as determined by fact checking is utilized whengenerating a summary. Furthering the example, lexical chains aregenerated using only sentences or phrases of factually accurate content.In another example, lexical chains are generated using the entirecontent, but only factually accurate sentences or phrases are selectedwhen generating the summary.

For example, only factually inaccurate content (and/or anothercharacterization) as determined by fact checking is utilized whengenerating a summary. Furthering the example, lexical chains aregenerated using only sentences or phrases of factually inaccuratecontent. In another example, lexical chains are generated using theentire content, but only factually inaccurate sentences or phrases areselected when generating the summary.

For example, factually accurate content, factually inaccurate content,and/or other characterizations of content as determined by fact checkingare utilized when generating two or more summaries (e.g., a summary offactually accurate information and a summary of factually inaccurateinformation).

For example, all content is fact checked and summarized, and the factualaccuracy results are indicated.

For example, only content from an entity with a validity rating above athreshold is used to generate content.

For example, only content from an entity with a validity rating above athreshold is aggregated.

For example, only content from an entity with a validity rating above athreshold is summarized.

For example, only content from an entity with a satisfaction ratingabove a threshold is used to generate content.

For example, only content from an entity with a satisfaction ratingabove a threshold is aggregated.

For example, only content from an entity with a satisfaction ratingabove a threshold is summarized.

For example, factually accurate information as determined by factchecking is used to generate content, only content with a factuallyaccuracy rating above a threshold is aggregated, and additionalinformation is fact checked.

For example, factually accurate information as determined by factchecking is used to generate content, only content with a factuallyaccuracy rating above a threshold is aggregated, which are thensummarized, and additional information is fact checked and summarized.

For example, content is aggregated based on a keyword, the aggregatedcontent is fact checked, and only the top 2 items of content based onfactual accuracy ratings are summarized and merged.

As described herein, these are merely examples, any combination orpermutation of any of the methods/method steps with any combination orpermutation of input/output is able to be utilized together.

FIG. 12 illustrates a flowchart of a method of generating and factchecking content according to some embodiments. In the step 1200,content is generated. In the step 1202, the generated content is factchecked. For example, fact check results are displayed with thegenerated content. In some embodiments, fewer or additional steps areimplemented. In some embodiments, the order of the steps is modified.

FIG. 13 illustrates a flowchart of a method of aggregating and factchecking content according to some embodiments. In the step 1300,content is aggregated. In the step 1302, the aggregated content is factchecked. For example, the aggregated content is displayed with factcheck results. In another example, the aggregated content issorted/ordered based on the fact check results (e.g., content withhighest factual accuracy rating is displayed first). In another example,aggregated content that does not meet a factual accuracy threshold isnot displayed. In some embodiments, fewer or additional steps areimplemented. In some embodiments, the order of the steps is modified.

FIG. 14 illustrates a flowchart of a method of summarizing and factchecking content according to some embodiments. In the step 1400,content is summarized. In the step 1402, the summarized content is factchecked. For example, only a summary of the content is fact checked, andresults are displayed with the summary. In another example, only thesummary is fact checked, but the fact check results are displayed withthe entire content. In some embodiments, fewer or additional steps areimplemented. In some embodiments, the order of the steps is modified.

FIG. 15 illustrates a flowchart of a method of fact checking content andgenerating content according to some embodiments. In the step 1500,content is fact checked. In the step 1502, content is generated usingthe fact check analysis. For example, only factually accurateinformation is utilized when generating content. In some embodiments,fewer or additional steps are implemented. In some embodiments, theorder of the steps is modified.

FIG. 16 illustrates a flowchart of a method of fact checking content andaggregating content according to some embodiments. In the step 1600,content is fact checked. In the step 1602, content is aggregated usingthe fact check analysis. For example, only content with a factualaccuracy rating above a threshold is aggregated. In some embodiments,fewer or additional steps are implemented. In some embodiments, theorder of the steps is modified.

FIG. 17 illustrates a flowchart of a method of fact checking content andsummarizing content according to some embodiments. In the step 1700,content is fact checked. In the step 1702, content is summarized usingthe fact check analysis. For example, only content with a factualaccuracy rating above a threshold is summarized. In another example, thefact checking results are utilized with lexical chaining to generate asummary. In some embodiments, fewer or additional steps are implemented.In some embodiments, the order of the steps is modified.

FIG. 18 illustrates a flowchart of a method of generating, aggregating,summarizing and/or fact checking content according to some embodiments.In the step 1800, content is generated, aggregated, summarized and/orfact checked. In the step 1802, the output of the step 1800 and/or thecontent and/or other content is utilized for generating, aggregating,summarizing and/or fact checking. In some embodiments, fewer oradditional steps are implemented. In some embodiments, the order of thesteps is modified.

FIG. 19 illustrates a flowchart of a method of generating, aggregating,summarizing and/or fact checking content according to some embodiments.In the step 1900, content is generated, aggregated, summarized and/orfact checked. In the step 1902, the output of the step 1900 and/or thecontent and/or other content is utilized for generating, aggregating,summarizing and/or fact checking. In the step 1904, the output of thestep 1900 and/or the output of the step 1902 and/or the content and/orother content is utilized for generating, aggregating, summarizingand/or fact checking. In some embodiments, fewer or additional steps areimplemented. In some embodiments, the order of the steps is modified.

FIG. 20 illustrates a flowchart of a method of generating, aggregating,summarizing and/or fact checking content according to some embodiments.In the step 2000, content is generated, aggregated, summarized and/orfact checked. In the step 2002, the output of the step 2000 and/or thecontent and/or other content is utilized for generating, aggregating,summarizing and/or fact checking. In the step 2004, the output of thestep 2000 and/or the output of the step 2002 and/or the content and/orother content is utilized for generating, aggregating, summarizingand/or fact checking. In the step 2006, the output of the step 2000and/or the output of the step 2002 and/or the output of the step 2004and/or the content and/or other content is utilized for generating,aggregating, summarizing and/or fact checking. In some embodiments,fewer or additional steps are implemented. In some embodiments, theorder of the steps is modified.

FIG. 21 illustrates a diagram of implementations usable with or withoutoptimized fact checking according to some embodiments. Theimplementations include: generating content, aggregating content,merging content, summarizing content, determining opposing perspectives,utilizing validity ratings, utilizing satisfaction ratings, replay,breaking news, utilizing language differences, advertising,incorporating user interests, cloud computing, augmented reality,search, and/or any other implementation. The implementations are able tobe utilized separately or together with any of the otherimplementations.

FIG. 22 illustrates a diagram of an augmented reality device accordingto some embodiments. The device 2200 includes a frame 2202, a lens 2204,and a camera 2206. In some embodiments, the lens 2204 or part of thelens 2204 is used as a display or a separate display is able to beincluded with the device 2200. The camera 2206 is able to acquire visualcontent by scanning and/or taking a picture of objects. In someembodiments, the camera 2206 is capable of processing the data includingconverting the content to text, parsing the content, generating content,aggregating content, summarizing content, fact checking the contentand/or providing supplemental information, and indicating a result ofthe fact checking/supplemental information search on the lens 2204 oranother location. In some embodiments, the camera 2206 acquires thecontent, and some or all of the processing, fact checking, searchingand/or indicating occurs on another device (e.g., in the cloud). Forexample, the camera 2206 acquires a restaurant name, sends (using atransceiver/receiver) the content or identifying information to thecloud for converting, parsing, fact checking, and then the could sendsthe results to the camera 2206 (or directly to the lens 2204) fordisplay on the lens 2204 or elsewhere. In another example, a processor2208 (and/or any other circuitry/computer components) is also includedwith the glasses and is coupled to the camera 2206 and lens 2204, andthe processor 2208 processes and fact checks the information and sendsthe result to the lens 2204. The device 2200 is able to include amicrophone 2210 for acquiring audio. The device 2200 is able to be anyshape or configuration and is not limited to the specific configurationas shown. For example, the frame is able to be any shape, the lens isable to be singular or multiple lenses in any shape, the camera is ableto be located anywhere and does not even need to be attached to theframe (e.g., a camera is worn on the user's head, but information isdisplayed on the lens), or the processor is located elsewhere.

For example, a user wears the augmented reality device 2200 to apolitical rally. The camera 2206 of the device 2200 uses facialrecognition to determine that Politician J is speaking. The microphone2210 receives what Politician J is saying. The processor 2208 processesthe received content and converts it into text. The text is factchecked. In some embodiments, the fact check results are classified(e.g., factually accurate and factually inaccurate). Using the factchecked information, a summary is generated only including factuallyaccurate content. In another example, an article is generated using onlyfactually accurate content. In another example, two articles aregenerated, one with only factually accurate content, and one with onlyfactually inaccurate content. In some embodiments, the summary orsummaries are generated using lexical chaining and/or othersummarization implementations. The summary or article generated isdisplayed on the lens 2204 for the user to read, or the generatedcontent is published such as sent to the Web (e.g., to the user's blogor social networking page or to be published on a media site)

In another example, a user wears the augmented reality device 2200. Thecamera 2206 of the device 2200 uses text recognition to determine thename of a restaurant (e.g., an image is taken of the name, the image isprocessed using an application to convert an image into text). Forexample, the user's augmented reality device 2200 detects Restaurant X,and instead of or in addition to providing a list of Yelp reviews, anautomatically generated summary of the Yelp reviews is provided to theuser on the lens 2204 of the augmented reality device 2200. In anotherexample, articles about Restaurant X are aggregated and displayed on thelens 2204. In another example, after detecting Restaurant X, the user isable to send social networking messages about the restaurant.

The augmented reality device is able to be any device such as glasses,goggles, ear buds (or another hearing device), a tactile device (e.g., adevice to vibrate a part of the user such as finger or ear), and/or anyother device to provide augmented reality. For example, augmentedreality glasses include a tactile device which vibrates the earpiecenear the users ear upon determining a triggering event such as factchecking content and determining it is factually inaccurate. In someembodiments, the augmented reality device includes a microphone toreceive audio, and the audio is processed (e.g., converted to text) andfact checked and/or analyzed as described herein. In some embodiments,the augmented reality device includes a projection device or theprojection device is the camera or part of the camera. For example, theprojection device is able to output any of the results or informationonto another object (e.g., wall, paper, table). Any of the steps/methodsdescribed herein are able to be implemented using the augmented realitydevice.

In some embodiments, optimized fact checking includes using a broadeningimplementation approach. For example, an exact match fact check isimplemented. The exact phrase is searched for within source information.If the exact phrase is found in a sufficient number of sources (e.g.,above a lower threshold), then a result is returned (e.g., true). If theexact phrase is not found (e.g., equal to or below the lower thresholdof sources), then a second fact check implementation is utilized. Forexample, the second fact check implements a pattern matching search. Thepattern matching search is able to be implemented in any manner, forexample, pattern matching utilizes subject-verb-object matching todetermine if the same or a similar item matches. If the second factcheck returns with sufficient confidence (e.g., number of matches and/orsources above a lower threshold or a pattern matching confidence scoreabove a lower threshold), then a result is returned. If the second factcheck does not return with sufficient confidence, then a third factcheck is implemented. For example, the third fact check implements anatural language search. If the third fact check returns with sufficientconfidence (e.g., above a natural language confidence lower threshold ornumber of matches above a lower threshold), then a result is returned.If the third fact check does not return with sufficient confidence, thena negative result or some other result is returned, or no result isreturned. Although only three fact check implementations are describedherein, any number of implementations are able to be implemented andused before returning a negative result.

In some embodiments, optimized fact checking includes using a broadeningsource approach according to some embodiments. Information to be factchecked is compared with a narrow source. For example, the narrow sourceis a single database stored on a local device. Other examples of narrowsources are a single document, a single document on a local device, adesignated fact checking database or a single web page. If the factcheck determines a sufficient result (e.g., information matches or isverified by the narrow source), then a result is returned (e.g.,validated). If the fact check does not find a match, then a broaderscope of sources is utilized (e.g., many databases stored within adedicated set of “cloud” devices). If a sufficient result is determined(e.g., a number above a lower threshold of sources agree or disagreewith the information to be fact checked), then a result is returned. Ifthe broader fact check does not find a match, then an even broader scopeof sources is utilized (e.g., the entire Web). If a sufficient result isdetermined (e.g., a number above a lower threshold of sources agree ordisagree with the information to be fact checked), then a result isreturned. If the even broader fact check does not find a match, then aresult indicating a negative result, some other result, or no result isindicated. In some embodiments, additional broadening is implemented. Inother words, the fact checking is not limited to three rounds ofsources.

In some embodiments, optimized fact checking utilizes sources on devicesof differing speeds. For example, source information on a local cache isused first, then source information on a Random Access Memory (RAM) isused, then source information on a hard drive is used, and then sourceinformation on distributed storage is used last for fact checking. Inthis example, quicker (meaning faster access times) sources are usedfirst, followed by slower sources if a result is not found. In someembodiments, most recent, most popular, and/or most commonquotes/information are stored in the quicker storage (e.g., cache), andthe least recent, least popular, least common quotes/information arestored in the slower storage (e.g., distributed storage). In someembodiments, common variants of recent, popular, and/or commoninformation are stored (locally) as well. For example, a similar commentwith one or two of the words changed or replaced by synonyms. In someembodiments, only facts are stored in the cache or in devices fasterthan a hard drive (e.g., cache and RAM). In some embodiments, only factsare stored on faster devices (e.g., a hard drive or faster) and onlyopinions are stored on slower devices (e.g., distributed sourcedevices). In some embodiments, higher confidence score fact checkresults are stored on devices with faster access times. For example, ifa cache is able to store 1,000 fact check results, the 1,000 fact checkresults with the highest confidence score are stored in the cache, andthe next highest fact check results are stored on RAM, and so on withthe lowest confidence score results stored on the slowest devices. Inanother example, the storage includes varying speeds of servers, harddrives, locations on hard drives, and other data storage devices. Forexample, the most popular or relevant sources are stored on an extremelyfast server, and less popular/relevant sources are stored on a slowerserver, and even less popular/relevant sources are stored on an evenslower server.

In some embodiments, optimized fact checking includes using akeyword-based source approach according to some embodiments. A keywordor keywords (or phrases) are detected in the information. For example,it is detected that a commentator is discussing “global warming” and“taxes.” Initially, only source information classified by the keywords“global warming” and “taxes” are utilized for fact checking. Instead oflooking at all sources to confirm any information, the sourceinformation utilized is found in source information classified asrelated to “global warming” and “taxes.” If the comparison using thesource information produces a sufficient result, then the result isreturned, and the process ends. If the comparison does not produce aresult, then, the comparison uses broader sources such as sources thatare related to only a single keyword such as “taxes.” If that comparisonreturns a sufficient result, then the process ends. If furthercomparisons are needed, then the scope of source information broadensagain, and sources classified with keywords related to keywords found inthe information are used. For example, instead of just using “taxes” and“global warming,” sources under classifications of: “economics,”“finance,” and “weather” are utilized. If a result is found, then theresult is returned. In some embodiments, if a result is still not found,the process ends, with a negative result or no result returned, and insome embodiments, further expansion of the sources is implemented.

In some embodiments, the optimized fact checking implementations and/orother implementations are utilized together. For example, an exact matchsearch is implemented on a local cache using only keyword-classifiedsource information before any other comparisons are performed, and thecomparisons broaden/narrow from there. Additional examples include:exact match on cache, then exact match on RAM, exact match on harddrive, exact match on other locations, then pattern matching on cache,pattern matching on RAM, pattern matching on other locations, thennatural language search on cache, natural language search on RAM, andnatural language search on other locations. In another example, an exactmatch on cache is used first, then a pattern match on cache, then anatural language search on cache, then an exact match on RAM, and so on.Any combination of the efficient searches/fact checking is possible. Insome embodiments, the fact checking process continues until a result isfound or a timeout is determined (e.g., after 0.5 ms of searching and noresult is determined, the process times out).

In some embodiments, the optimized fact checking system (and/or any ofthe methods described herein) is a smart phone application including,but not limited to, an iPhone®, Droid® or Blackberry® application. Insome embodiments, a broadcaster performs the generating, aggregating,summarizing and/or fact checking. In some embodiments, a user'stelevision performs the generating, aggregating, summarizing and/or factchecking. In some embodiments, a user's mobile device performs thegenerating, aggregating, summarizing and/or fact checking and causes(e.g., sends) the results to be displayed on the user's televisionand/or another device. In some embodiments, the television sends thefact checking result and/or other content to a smart phone.

Utilizing the optimized fact checking system, method and device dependson the implementation to some extent. In some implementations, atelevision broadcast uses fact checking to fact check what is said orshown to the viewers, and a mobile application, in some embodiments,uses fact checking to ensure a user provides factually correctinformation. Other examples include where web pages or social networkingcontent (e.g., tweet or Facebook® page) are processed, fact checked inan optimized manner, and a result is provided. The generating,aggregating, summarizing and/or fact checking is able to be implementedwithout user intervention. For example, if a user is watching a newsprogram, the generating, aggregating, summarizing and/or fact checkingis able to automatically occur and present the appropriate information.In some embodiments, users are able to disable the generating,aggregating, summarizing and/or fact checking if desired. Similarly, ifa user implements generating, aggregating, summarizing and/or factchecking on his mobile application, the generating, aggregating,summarizing and/or fact checking occurs automatically. For a newscompany, the generating, aggregating, summarizing and/or fact checkingis also able to be implemented automatically, so that once installedand/or configured, the news company does not need take any additionalsteps to utilize the generating, aggregating, summarizing and/or factchecking. In some embodiments, the news company is able to takeadditional steps such as adding sources. In some embodiments, newscompanies are able to disable the generating, aggregating, summarizingand/or fact checking, and in some embodiments, news companies are notable to disable the generating, aggregating, summarizing and/or factchecking to avoid tampering and manipulation of data. In someembodiments, one or more aspects of the generating, aggregating,summarizing and/or fact checking are performed manually.

In operation, the optimized fact checking system, method and deviceenable information to be fact checked in real-time and automatically(e.g., without user intervention) in an optimized manner. Themonitoring, processing, fact checking and providing of status are eachable to occur automatically, without user intervention. Similarly,generating, aggregating, and/or summarizing are able to occurautomatically, without user intervention. Results of the fact checking(and/or any methods) are able to be presented nearly instantaneously, sothat viewers of the information are able to be sure they are receivingaccurate and truthful information. Additionally, the fact checking isable to clarify meaning, tone, context and/or other elements of acomment to assist a user or viewer. By utilizing the speed and breadthof knowledge that comes with automatic, computational fact checking, theshortcomings of human fact checking are greatly overcome. Withinstantaneous or nearly instantaneous fact checking, viewers will not beconfused as to what information is being fact checked since the resultsare posted instantaneously or nearly instantaneously versus when a factcheck is performed by humans and the results are posted minutes later.The rapid fact checking provides a significant advantage over past dataanalysis implementations. Any of the steps described herein are able tobe implemented automatically or manually. Any of the steps describedherein are able to be implemented in real-time or non-real-time. Thethresholds described herein are able to be determined/generated in anymanner such as user-generated/specified, automatically generated, and/orgenerated based on empirical data/testing.

The methods, systems, and devices described herein provide manyimprovements such as automatically generating, aggregating, summarizingand/or fact checking content quickly and in an optimized manner. Theimprovements also include providing factually accurate content (e.g., bygenerating a story using fact checked content, aggregating onlyfactually accurate content or only using factually accurate content togenerate a summary), providing fact check results to indicate the statusof content, and many other improvements. By summarizing, and thengenerating, aggregating, and/or fact checking, less content is analyzed,thus efficiency is improved. By fact checking then generating,aggregating, and/or summarizing, accurate content or another specificset of content is used, so the output is more accurate and is producedin a more efficient manner, since less content is analyzed. Theimprovements involve providing data to users more quickly, moreefficiently, and more accurately. The improvements are also able toreduce storage space, bandwidth usage, processing burdens, and displayscreen footprint.

Examples of Implementation Configurations:

Although the monitoring, processing, generating, aggregating,summarizing, and/or, fact checking and indicating are able to occur onany device and in any configuration, these are some specific examples ofimplementation configurations. Monitoring, processing, generating,aggregating, summarizing, and/or, fact checking and providing all occuron a broadcaster's devices (or other emitters of information including,but not limited to, news stations, radio stations and newspapers).Monitoring, processing and generating, aggregating, summarizing, and/or,fact checking occur on a broadcaster's devices, and providing occurs onan end-user's device. Monitoring and processing occur on a broadcaster'sdevices, generating, aggregating, summarizing, and/or, fact checkingoccurs on a broadcaster's devices in conjunction with third-partydevices, and providing occurs on an end-user's device. Monitoring occurson a broadcaster's devices, processing and providing occur on anend-user's device, and generating, aggregating, summarizing, and/or,fact checking occurs on third-party devices. Monitoring, processing,generating, aggregating, summarizing, and/or, fact checking, andproviding all occur on third-party devices. Monitoring, processing,generating, aggregating, summarizing, and/or, fact checking, andproviding all occur on an end-user's device. Monitoring, processing andgenerating, aggregating, summarizing, and/or, fact checking occur on asocial networking site's device, and providing occurs on an end-user'sdevice. These are only some examples; other implementations arepossible. Additionally, supplemental information is able to be monitoredfor, searched for, processed and/or provided using any of theimplementations described herein. Further, generating, aggregating,summarizing and/or fact checking are able to be implemented on anydevice or any combination of devices.

Fact checking includes checking the factual accuracy and/or correctnessof information. The type of fact checking is able to be any form of factchecking such as checking historical correctness/accuracy, geographicalcorrectness/accuracy, mathematical correctness/accuracy, scientificcorrectness/accuracy, literary correctness/accuracy, objectivecorrectness/accuracy, subjective correctness/accuracy, and/or any othercorrectness/accuracy. Another way of viewing fact checking includesdetermining the correctness of a statement of objective reality or anassertion of objective reality. Yet another way of viewing fact checkingincludes determining whether a statement, segment or phrase is true orfalse.

Although some implementations and/or embodiments have been describedrelated to specific implementations and/or embodiments, and someaspects/elements/steps of some implementations and/or embodiments havebeen described related to specific implementations and/or embodiments,any of the aspects/elements/steps, implementations and/or embodimentsare applicable to other aspects/elements/steps, implementations and/orembodiments described herein.

The present invention has been described in terms of specificembodiments incorporating details to facilitate the understanding ofprinciples of construction and operation of the invention. Suchreference herein to specific embodiments and details thereof is notintended to limit the scope of the claims appended hereto. It will bereadily apparent to one skilled in the art that other variousmodifications may be made in the embodiment chosen for illustrationwithout departing from the spirit and scope of the invention as definedby the claims.

What is claimed is:
 1. A method programmed in a non-transitory memory ofa device comprising: aggregating content on a single topic by searchingfor online news articles and social networking information, wherein thesingle topic is determined by comparing dates and matching keywordswithin the content; classifying a first portion of the content as havinga first perspective; classifying a second portion of the content ashaving a second perspective; generating new content comprising the firstportion of the content classified as having the first perspective andthe second portion of the content classified as having the secondperspective, wherein the new content is tailored to each user based onuser interests.
 2. The method of claim 1 further comprising generating asummary of the new content.
 3. The method of claim 1 further comprisingfact checking the new content and eliminating any sentences within thenew content that are determined to be factually inaccurate.
 4. Themethod of claim 1 further comprising fact checking the new content andeliminating any sentences within the new content that are determined tobe factually accurate.
 5. The method of claim 1 wherein sources of thecontent are classified as having the first perspective or the secondperspective.
 6. The method of claim 1 wherein the content comprisesbreaking news.
 7. The method of claim 6 wherein the new content includesa representation that the new content is unverified.
 8. The method ofclaim 6 wherein a number of times the breaking news has been re-tweetedis displayed.
 9. The method of claim 6 wherein a number of sources forthe breaking news is displayed.
 10. The method of claim 6 whereinsources classified as having an opposing view are analyzed regarding thebreaking news, and if the sources agree on the breaking news, thebreaking news is indicated as being reported by opposing sources. 11.The method of claim 1 wherein generating the new content includeshighlighting key aspects of the single topic.
 12. The method of claim 1wherein generating the new content includes generating a headline and aphotograph to be provided with the new content.
 13. The method of claim1 wherein the single topic is determined by counting a number ofmatching keywords within the content.
 14. The method of claim 1 whereinthe single topic is determined by matching keywords in a title withtopic keywords stored in a database.
 15. The method of claim 1 whereinclassifying the content as having the first perspective and classifyingthe second portion of the content as having the second perspective is bymatching an author of the content with a database storing names andcorresponding perspectives.
 16. The method of claim 1 wherein thecontent aggregated is based on a number of likes and dislikes.
 17. Themethod of claim 1 wherein the single topic is based on a search stringinput to a search engine, and search results are displayed in additionto the new content.
 18. The method of claim 1 wherein the single topicis based on social networking information of a user to determine afocus.
 19. A method programmed in a non-transitory memory of a devicecomprising: classifying a first portion of breaking news as having afirst perspective; classifying a second portion of the breaking news ashaving a second perspective; and generating new content comprising thefirst portion of the breaking news classified as having the firstperspective and the second portion of the breaking news classified ashaving the second perspective, wherein the new content is tailored toeach user based on user interests.
 20. A device comprising: anon-transitory memory for storing an application for automaticallyperforming the following steps: aggregating content on a single topic bysearching for online news articles and social networking information,wherein the single topic is determined by comparing dates and matchingkeywords within the content; and classifying a first portion of thecontent as having a first perspective; classifying a second portion ofthe content as having a second perspective; and generating new contentcomprising the first portion of the content classified as having thefirst perspective and the second portion of the content classified ashaving the second perspective, wherein the new content is tailored toeach user based on user interests; and a processor for processing theapplication.